CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
Abstract: Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns implemented functionality in existing codebases into executable RL environments using source code as its only task-specific input. CodeMidas allocates agentic compute to every stage of environment construction: agents explore implemented functionality to formulate behavioral specifications, construct tests grounded in execution of the original code, and validate and filter candidate tasks through execution checks and repeated solution rollouts. The resulting dataset has 5,545 training tasks from 3,185 open-source codebases spanning 23 programming languages and 15 technical domains. Training MiMo-V2.5 on these tasks with GRPO improves performance on all five diverse benchmarks, covering issue repair (DeepSWE + 11.7%), whole-program construction (ProgramBench +17%), and terminal work (Terminal-Bench v2.1 +8.5%). Ablations show that increasing the number of high-quality training tasks improves performance. Trajectory analysis shows the RL-trained agent demonstrates better behaviors like increasing codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.
First 10 authors:
Summary
- The paper introduces an agentic coding pipeline centralizing task generation, execution, and verification directly from source code in a variety of open-source environments
- Training on the resulting 5,545-task dataset improves model performance on multiple benchmarks, with significant gains on DeepSWE (11.7% increase) and ProgramBench (17.0% increase) on the Almost Solved metric
- Advanced post-rollout filtering and unique task generation algorithms from source code only are instrumental in generating high quality tasks making it valuable for a variety of RL research
Problem formulation and contribution
CodeMidas addresses a central bottleneck in coding-agent reinforcement learning: constructing large collections of executable environments with both diverse task distributions and reliable verifiers. Existing environment-generation pipelines commonly depend on development artifacts such as issues, pull requests, commits, documentation, or pre-existing tests. This dependence restricts task coverage to functionality that has been documented, modified, or tested in a suitable form. CodeMidas instead treats implemented functionality in source repositories as the primary task substrate. Its central claim is that source code alone can support the construction of task statements, development environments, reference solutions, and executable verifiers.
The paper introduces an agentic pipeline that transforms functionality in open-source codebases into RL environments. Each resulting task comprises a natural-language specification, a containerized codebase in which the target implementation has been removed or modified, and a hidden executable verifier. The solver receives the specification and adapted repository but not the verifier or reference implementation. Grading returns a binary execution reward based on hidden tests. The complete pipeline includes task design and codebase adaptation, execution-grounded test construction, environment consistency checking, and post-rollout filtering (2609.22068).
The resulting dataset contains 5,545 tasks from 3,185 open-source codebases, spanning 23 programming languages and 15 technical domains. Training MiMo-V2.5 with GRPO on this dataset improves performance on all five reported external benchmarks, including repository-level repair, whole-program construction, code translation, and terminal interaction. The strongest absolute gains are reported on DeepSWE, where pass rate increases from 10.0% to 21.7%, and ProgramBench, where the Almost Solved score rises from 4.5 to 21.5. Terminal-Bench v2.1 increases from 63.7% to 72.2%.
Environment construction from source code
The distinctive methodological decision is to use source code as the only task-specific input. CodeMidas does not require an issue, commit, pull request, written requirement, or existing test suite for an individual task. This positions the method differently from pipelines such as SWE-bench, R2E-Gym, SWE-smith, SWE-Flow, and related systems, which derive tasks from development histories, tests, or documentation. The source-only formulation is important because implemented behavior can expose functionality that was never isolated in a development artifact.
Task construction begins with an agentic analysis of repository structure and build metadata. The system identifies functionality with public entry points and observable effects, including command-line interfaces, pure library functions, and stateful APIs. The task-design agent traces entry points and shared dependencies, determines the scope of the target functionality, removes the core implementation, and modifies the surrounding repository to produce a coherent development starting point. The original implementation is retained separately as a reference solution.
This procedure makes the task specification and code boundary jointly constructed. The statement describes required inputs, observable behavior, and public interfaces, while leaving implementation strategies and internal abstractions unconstrained. The approach therefore attempts to preserve the distinction between behavioral equivalence and source-level similarity. This is essential for avoiding a verifier that rewards reproduction of the reference patch rather than satisfaction of the stated functionality.
The dataset has substantial but uneven coverage. Python accounts for 21.4% of tasks, TypeScript for 18.3%, Go for 16.2%, C++ for 12.5%, and JavaScript for 11.3%. The ten most frequent languages account for 5,445 of 5,545 tasks, or 98.2%, although the complete collection spans 23 languages. Systems software, web technologies, and developer tools are the three largest domains, together representing 45.6% of the dataset.
Figure 1: CodeMidas decomposes environment construction into task design, test construction, execution consistency, and post-rollout filtering.
The task granularity is oriented toward nontrivial repository-level implementation. Reference patches have a median size of 142 source lines, with an interquartile range of 66–305 lines. In 65.9% of tasks, the reference patch touches at least two source files. These statistics indicate that the dataset is not limited to isolated function completion, although patch size is only a coarse proxy for semantic and interaction complexity.
Figure 2: The training set covers 23 programming languages, with Python, TypeScript, Go, C++, and JavaScript constituting the largest language groups.
Figure 3: Reference-solution sizes are distributed broadly on a logarithmic scale, with a median of 142 lines and substantial multi-file task coverage.
Execution-grounded verifier construction
Verifier construction is treated as a separate agentic problem rather than as a direct reuse of existing project tests. An agent maps the behavioral requirements in the task statement to inputs, boundary cases, and expected outcomes. It executes the original implementation in a reference copy of the repository and records the resulting behavior. The procedure supports process-level tests for command-line tools, input-output assertions for pure functions, and multi-call sequences for stateful APIs.
The paper distinguishes specified behavior from incidental reference behavior. Where the statement fixes an output or property, the verifier asserts the corresponding result. Where the statement leaves details unspecified, the verifier checks only the stated constraint. For example, a required exception type may be tested without constraining message wording. Similarly, tests may validate semantic properties without enforcing incidental ordering or internal structure.
An additional review stage examines every assertion for overconstraint. Assertions tied to private symbols, exact wording, incidental ordering, or implementation-specific structure are removed or replaced with externally observable behavioral checks. A task is rejected when an unsupported assertion lacks a behavioral substitute. The revised verifier is then rerun on the reference implementation to ensure that test construction has not introduced incompatibility.
This process directly confronts the test-oracle problem: a test can be executable and still fail to represent the task specification. The paper’s use of execution traces provides a practical way to instantiate expected outputs, but it does not eliminate the need for specification review. Reference execution establishes what the original implementation does; it does not by itself establish which aspects of that behavior are required.
The environment-preparation stage further attempts to prevent accidental leakage. Dependencies and build resources are installed according to project declarations, while compiled outputs, caches, construction artifacts, installed copies, and original tests related to the target functionality are removed. The verifier remains outside the solver-visible environment and is injected only during grading.
Each candidate task undergoes an execution-consistency check in six fresh containers: two executions with the incomplete starting repository and four with the reference solution. Both starting-state executions must fail, and all four reference executions must pass. This requirement filters unstable tasks and enforces the intended fail-to-pass transition. It also makes the verifier’s runtime behavior part of the data-quality criterion rather than an assumption.
Post-rollout filtering and dataset quality
CodeMidas uses model-generated rollouts to identify defects that static construction and execution consistency checks may miss. The filtering process has three components.
First, adversarial rollouts search for residual leakage. An agent inspects the complete solver-visible environment, including caches, compiled artifacts, construction leftovers, and installed project copies, and records commands that could expose or recover the removed implementation. A separate review determines whether the alleged exploit can bypass the intended development work. Confirmed leakage causes rejection.
Second, solution-review rollouts assess agreement between agent judgments and executable test outcomes. Four coding attempts are generated for each task. A reviewing agent examines the implementations, trajectories, test outputs, specification, verifier, and reference solution. It identifies false positives, in which an incorrect solution passes, and false negatives, in which a correct solution fails. Tasks with verifier defects are discarded.
Third, outcome filtering retains tasks on which a frontier model produces both successful and failed attempts under the available rollout budget. An all-pass result may indicate weak tests or an overly easy task, whereas an all-fail result may indicate excessive difficulty, underspecified requirements, or a defective environment. The procedure does not identify the cause of an all-pass or all-fail outcome, but it removes these ambiguous cases from the training set.
The resulting dataset therefore represents a quality-filtered subset rather than the maximum number of extractable tasks. This distinction is central to the paper’s empirical argument: a smaller collection of reliable environments can be more effective than a larger unfiltered collection.
Reinforcement-learning results
The authors train MiMo-V2.5 with GRPO using binary verifier outcomes, a batch size of 32, and 32 rollouts per task. The maximum response length is 516,096 tokens and the maximum rollout length is 500 turns, reflecting the long-horizon interaction regime targeted by the environments. CodeMidas Val contains 200 held-out tasks, disjoint from both the training set and the five external benchmarks.
RL improves every reported external evaluation. The results are summarized below.
| Evaluation | Initial policy | CodeMidas RL | Absolute gain |
|---|---|---|---|
| DeepSWE v1.1 pass rate | 10.0% | 21.7% | +11.7 points |
| Terminal-Bench v2.1 pass rate | 63.7% | 72.2% | +8.5 points |
| ProgramBench Almost Solved | 4.5% | 21.5% | +17.0 points |
| CodeMidas Val pass rate | 35.0% | 44.7% | +9.7 points |
The paper also reports gains on SWE-bench Pro and RepoZero C2Rust, although the supplied text does not state their numerical initial and final scores. The cross-benchmark improvements are significant because the training environments are not limited to issue repair. They transfer to whole-program construction, repository repair, code translation, and terminal-based tasks.
Figure 4: GRPO training on CodeMidas improves MiMo-V2.5 across five external benchmarks and the held-out CodeMidas evaluation.
On CodeMidas Val, pass rate increases from 35.0% to 44.7%. The improvement is accompanied by longer trajectories, suggesting that the policy uses more of the available interaction budget. This association should not be interpreted as evidence that length alone causes the gain: the experiments do not isolate interaction length from the policy’s broader changes in exploration, drafting, and verification.
The magnitude of the ProgramBench result is particularly notable. The Almost Solved metric requires a solution to pass at least 95% of tests, so the increase from 4.5% to 21.5% reflects a substantial shift in near-complete program reconstruction rather than merely incremental test coverage. At the same time, the metric is not identical to exact full correctness, and the paper appropriately reports it separately from pass-rate evaluations.
Scaling task quality and quantity
The ablation study compares high-quality subsets of 1,000, 3,000, and 5,545 tasks against an approximately 8,000-task vanilla sample constructed before environment cleaning, execution-consistency checks, and post-rollout filtering. All settings use the same training configuration and checkpoint range.
Performance increases with the number of high-quality tasks. DeepSWE scores rise from 17.57 for the 1k pool to 19.05 for the 3k pool and 21.70 for the full pool. CodeMidas Val rises from 41.30 to 43.22 and then 44.73. The full pool leads at every evaluated checkpoint from step 40 through step 70, reaching 44.73 at the final reported point.
Figure 5: Increasing the high-quality pool from 1k to 3k to 5k improves SWE-bench Pro, DeepSWE, and CodeMidas Val performance, while the unfiltered 8k pool underperforms.
The comparison with the vanilla 8k sample provides the paper’s strongest evidence for data quality. The filtered 5k pool exceeds the unfiltered 8k sample by 0.59 percentage points on SWE-bench Pro, 4.59 points on DeepSWE, and 4.49 points on CodeMidas Val. Even the filtered 3k pool outperforms the vanilla 8k sample on all three evaluations.
The implication is not simply that filtering removes noisy examples. The cleaning and consistency procedures may also reduce reward corruption, eliminate leakage-based shortcuts, and improve the alignment between task statements and verifiers. Because the ablation changes several filtering components jointly, it supports the value of the complete quality-control pipeline but does not identify the marginal contribution of each individual filter.
Behavioral changes during RL
The authors analyze trajectories to determine whether the benchmark gains correspond to systematic changes in agent behavior. Three measures are used: codebase exploration, code drafting, and self-verification. Exploration counts distinct read and search requests before the first edit. Drafting measures the fraction of code fragments in Write/Edit payloads that appeared previously in reasoning. Self-verification counts distinct verification commands after the final repository edit.
Between early and late training, pre-edit read/search calls increase from 27.2 to 40.1, the drafting ratio increases from 0.358 to 0.629, and distinct post-edit verification commands increase from 2.03 to 2.53. The policy therefore becomes more exploratory, more likely to formulate code before applying edits, and more diverse in its final verification procedures.
Figure 6: A representative rollout combines caller inspection, code drafting, targeted editing, and verification under multiple flag settings.
Self-verification is also statistically associated with success. Within the same task and checkpoint on CodeMidas Val, rollouts containing agent-written and executed checks have a mean pass rate 4.2 percentage points higher than rollouts without them, with a 95% confidence interval of 1.8–6.6 points. The corresponding differences for exploration and drafting are smaller: +0.7 points for exploration, with a confidence interval spanning zero, and +1.95 points for drafting, with an interval whose lower endpoint is approximately zero.
These estimates support a specific interpretation: verification behavior is more closely associated with successful completion than raw exploratory activity in the reported analysis. They do not establish causality, since stronger rollouts may both verify more and be more likely to succeed.
The behavioral changes generalize to held-out benchmarks. Exploration increases on SWE-bench Pro from 23.1 to 35.5 read/search calls, on ProgramBench from 55.7 to 83.6, and on Terminal-Bench from 11.9 to 16.8. Drafting increases on SWE-bench Pro from 0.304 to 0.653 and on ProgramBench from 0.106 to 0.361. Verification diversity increases on SWE-bench Pro from 0.80 to 0.96 and on Terminal-Bench from 2.01 to 2.61.
Interaction length changes in task-dependent ways. On SWE-bench Pro, mean assistant turns increase from 37.3 to 50.1, whereas on ProgramBench they decrease from 155.1 to 122.8. Thus, increased exploration does not uniformly produce longer trajectories. In whole-program construction, the trained policy explores more while completing tasks in fewer turns, which is consistent with more targeted interaction but does not by itself demonstrate improved planning efficiency.
Limitations and open questions
The paper’s conclusions depend on the reliability of agent-generated specifications and verifiers. Execution consistency verifies that the starting state fails and the reference implementation passes, but this criterion cannot establish that the hidden tests fully capture the natural-language requirements. Post-rollout review mitigates false positives and false negatives, yet it remains model-mediated and may fail to detect subtle specification gaps or semantically incorrect accepted solutions.
The source-only formulation also does not imply uniform coverage. The ten most frequent languages account for 98.2% of tasks, and three technical domains account for nearly half of the dataset. Consequently, the empirical evidence supports broad cross-task transfer but does not establish comparable effectiveness across all 23 languages or all 15 domains.
The quality-versus-scale ablation compares a jointly filtered dataset with a jointly unfiltered one. It therefore cannot determine whether the main benefit arises from environment cleanup, execution-consistency checks, leakage filtering, verifier review, outcome filtering, or their interaction. Nor does it establish how performance scales beyond 5,545 tasks.
Finally, the behavioral analysis is correlational. The association between self-verification and success is compatible with self-verification being beneficial, but also with better trajectories being more likely to contain verification. A controlled intervention that constrains or augments verification behavior would be required to distinguish these explanations. The paper also leaves open whether the observed transfer depends on MiMo-V2.5-specific capabilities, GRPO hyperparameters, or the particular external benchmark composition.
Conclusion
CodeMidas presents a source-code-centered method for constructing executable coding RL environments without relying on issues, commits, documentation, or existing tests as task-specific inputs. Its agentic pipeline combines behavioral task design, reference-execution-based test synthesis, environment consistency checks, leakage detection, verifier review, and rollout-based filtering.
Training on the resulting 5,545-task dataset improves MiMo-V2.5 across five external evaluations, with especially large gains on DeepSWE and ProgramBench. The ablations show that high-quality task scale matters: filtered 3k and 5k pools outperform a larger unfiltered 8k pool. Trajectory analysis further links RL training to increased codebase exploration, more explicit code drafting, and more diverse self-verification. The main unresolved question is how reliably these benefits persist when task specifications, verifier quality, language distributions, and model architectures differ from those used in the reported experiments (2609.22068).
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1. What is the paper about?
The paper introduces CodeMidas, a system for creating training exercises for AI coding agents.
Coding agents are artificial-intelligence programs that can read, write, and test computer code. To become better, they need lots of practice tasks, much like a student needs many exercises. The problem is that creating good coding exercises is difficult: each task needs clear instructions, a realistic coding environment, and reliable tests to decide whether the answer is correct.
CodeMidas tries to solve this problem by turning existing open-source software into new training tasks. It uses the code itself, rather than depending mainly on bug reports, written documentation, or old software changes.
2. What questions did the researchers study?
The researchers mainly wanted to know:
- Can existing source code be turned automatically into useful coding exercises?
- Can these exercises include accurate tests that fairly judge different correct solutions?
- Will an AI trained on these tasks become better at many kinds of programming work?
- Is it more useful to have a smaller number of carefully checked tasks than a larger number of poorly checked tasks?
- How does the AI’s behavior change while it learns?
In simple terms, the researchers were asking whether real software can be transformed into a large, reliable practice workbook for coding AIs.
3. How does CodeMidas work?
Each CodeMidas task contains three main parts:
- A written description of what the program should do.
- A coding environment where the AI must make changes.
- Hidden tests that check the completed work.
The hidden tests are not shown to the AI, just as a teacher might give students a problem without showing them the answer key.
Creating a task from existing code
CodeMidas starts with an open-source codebase. An AI system examines the project and looks for a useful function or feature that already works.
It then:
- Studies how the feature works.
- Writes a task description explaining the feature’s expected behavior.
- Removes the original implementation.
- Leaves the rest of the project available to the coding agent.
- Keeps the original implementation separately as a reference answer.
For example, imagine a program that already has a function for finding duplicate files. CodeMidas could remove that function and ask a coding agent to rebuild it.
The agent is expected to figure out its own programming strategy. The task description explains what the program must do, but not exactly how to do it.
Building fair tests
CodeMidas runs the original version of the program with different inputs and records what happens. These observations help create tests.
The tests may check:
- Whether a function gives the correct result.
- Whether a command-line program prints the required output.
- Whether an error is raised when incorrect input is given.
- Whether a program behaves correctly after several actions in a row.
The researchers try to avoid unfair tests. For example, if the task only requires a particular type of error, the test should not demand one exact error message unless the message was part of the instructions.
This is similar to grading a math problem: a teacher should accept different methods if they all produce the correct answer.
Checking that tasks are reliable
Before using a task for training, CodeMidas performs several checks:
- The incomplete version should fail the tests.
- The original complete version should pass the tests.
- These results must remain consistent in several fresh computer environments.
- AI agents try to solve the task several times.
- Other agents inspect whether the tests correctly judge those solutions.
- The system searches for hidden files, cached results, or compiled material that could accidentally reveal the answer.
This last check is called leakage filtering. It is like making sure students cannot find the answer key hidden under their desks.
Training the coding agent
The researchers trained an AI model called MiMo-V2.5 using 5,545 CodeMidas tasks.
They used reinforcement learning, a method in which an AI receives a reward for good behavior. Here, the reward was usually simple:
- The solution passes the hidden tests: reward.
- The solution fails the tests: no reward.
The researchers used a method called GRPO to help the model learn from groups of possible attempts. The technical details are less important than the main idea: the AI tried different solutions and learned to prefer the ones that worked.
4. What did the researchers find?
CodeMidas created a large and varied dataset
The system produced:
- 5,545 training tasks
- From 3,185 open-source codebases
- Covering 23 programming languages
- Spanning 15 technical areas
The most common languages included Python, TypeScript, Go, C++, and JavaScript. The tasks included software such as systems programs, web tools, and developer utilities.
This shows that CodeMidas can create tasks from many different kinds of software, not just one programming language or one type of problem.
Training improved the AI on several benchmarks
After training with CodeMidas, the coding agent performed better on five outside tests. These tests measured different skills, including:
- Repairing real software problems.
- Building complete programs.
- Translating code between languages.
- Working with command-line tools.
Some important results were:
| Evaluation | Before training | After training | Improvement |
|---|---|---|---|
| DeepSWE | 10.0% | 21.7% | +11.7 percentage points |
| Terminal-Bench | 63.7% | 72.2% | +8.5 percentage points |
| ProgramBench “Almost Solved” | 4.5% | 21.5% | +17 percentage points |
A percentage point difference compares two percentages directly. For example, moving from 10% to 21.7% is an increase of 11.7 percentage points.
The improvement on several different tests is important because it suggests that the AI did not simply memorize the CodeMidas exercises. Instead, it learned skills that could help with new programming tasks.
More high-quality tasks led to better results
The researchers compared training with 1,000, 3,000, and 5,545 carefully checked tasks.
In general, the AI performed better when it trained on more high-quality tasks. However, an especially interesting result was that the carefully cleaned 5,545-task collection performed better than a larger collection of about 8,000 tasks that had not gone through the same checks.
Even the carefully checked 3,000-task set performed better than the unfiltered 8,000-task set in several evaluations.
This suggests that quality matters as much as quantity. A large number of unreliable exercises may be less useful than a smaller number of trustworthy ones.
The AI changed how it solved problems
After training, the AI showed several new behaviors:
- It searched and read more of the codebase before editing.
- It planned more of its code before writing it.
- It used more varied tests to check its own work.
On CodeMidas tasks, the AI made about 27 read or search actions before its first edit early in training, compared with about 40 actions later in training.
Solutions that included the AI’s own checks had an average success rate about 4.2 percentage points higher than solutions without such checks.
This suggests that the AI became more careful. Instead of immediately changing code, it was more likely to investigate the project, plan a solution, and test its work.
5. Why are these findings important?
Training coding AIs usually requires many carefully designed examples. Human experts cannot easily create millions of realistic tasks by hand. CodeMidas offers a possible way to create more tasks automatically from the huge amount of software already available online.
The research also shows that source code contains useful information about:
- What a program is supposed to do.
- How its parts connect.
- What results users should expect.
- Which tests can be used to judge a solution.
If this approach continues to work, it could help build coding agents that are better at real software engineering, not just at solving small programming puzzles.
6. Simple conclusion
The paper’s main message is that existing software can be turned into practice exercises for AI coding agents.
CodeMidas removes a working feature from an open-source project, describes what that feature should do, creates hidden tests, and checks that the task is fair and reliable. The researchers used thousands of these tasks to train an AI, and the AI became better at several kinds of programming challenges.
The potential impact is significant: future coding agents could learn from a much wider range of realistic software tasks. However, the process still depends on automated systems making good descriptions and tests, so careful checking remains important. A task with a bad test could teach the AI the wrong lesson.
Overall, the paper suggests that well-checked training tasks made from real code can make AI programmers more capable, more careful, and better prepared for unfamiliar software problems.
Knowledge Gaps
The paper leaves the following knowledge gaps, limitations, and open questions unresolved:
- Generalization beyond MiMo-V2.5 is unknown: The experiments use a single base model and do not establish whether CodeMidas tasks improve other model architectures, parameter scales, or training paradigms.
- The causal contribution of CodeMidas is not fully isolated: Improvements may reflect GRPO, additional training compute, longer interaction budgets, or task exposure rather than the source-code-based task construction itself.
- The relative value of each pipeline component remains unclear: The study does not provide a complete factorial ablation separating task design, environment cleaning, execution-consistency checks, leakage filtering, verifier review, and rollout-outcome filtering.
- Verifier correctness is only partially validated: Reviewer-agent judgments and reference-solution agreement do not establish that tests cover all stated requirements or accept all valid alternative implementations.
- The reliability of model-based solution reviews is uncertain: The paper does not report inter-rater agreement, human validation rates, reviewer failure modes, or sensitivity to reviewer-model choice.
- The filtering process may introduce selection bias: Retaining tasks with both successful and failed frontier-model rollouts may preferentially select tasks of particular difficulty or structure and exclude genuinely easy, difficult, or novel tasks.
- Task difficulty is not systematically characterized: The dataset lacks a detailed analysis of difficulty distributions, factors determining difficulty, and whether training and evaluation tasks are matched or well calibrated.
- The evaluation sets are relatively small or incompletely reported: Statistical significance, confidence intervals, variance across random seeds, and per-task results are not reported for the main external benchmark improvements.
- Training stability and reproducibility are unresolved: The paper does not report results across multiple RL runs, random seeds, hyperparameter settings, or alternative rollout budgets.
- The comparison with the unfiltered 8k dataset is confounded: The vanilla and filtered pools may differ in task composition, language, difficulty, and reference-solution size, so the observed gains cannot be attributed solely to cleaning and filtering.
- Scaling beyond 5,545 tasks is not established: The paper shows gains from 1k to 5,545 tasks but does not determine whether performance continues to improve, saturates, or degrades with substantially larger task pools.
- Data efficiency is not quantified: The study does not compare CodeMidas with equivalent amounts of training tokens, environment interactions, or compute consumed by alternative task-generation methods.
- The role of task diversity is not disentangled from task quantity: It remains unclear whether gains arise from the number of tasks, language diversity, domain diversity, interface diversity, or repository-level complexity.
- Language coverage is highly imbalanced: Although 23 languages are represented, most tasks come from a few languages; performance and transfer to the long-tail languages are not evaluated separately.
- Domain coverage may not reflect real-world software distributions: The concentration in systems software, web technologies, and developer tools leaves performance on underrepresented domains unclear.
- The approach’s applicability to proprietary or non-public codebases is unknown: All tasks originate from open-source repositories, so licensing, access, dependency, and confidentiality constraints in industrial settings are not addressed.
- Potential training-data contamination is not thoroughly examined: The reference implementations, repositories, or generated tasks may overlap with pretraining or benchmark data, especially for widely used open-source projects.
- The effects of source-code memorization are not separated from genuine behavioral learning: The paper does not test whether agents solve tasks by recovering familiar implementations, exploiting repository-specific patterns, or learning transferable specifications.
- Security and supply-chain risks are underexplored: Building environments from arbitrary repositories and installing their dependencies may expose the pipeline to malicious code, compromised packages, unsafe build scripts, or sandbox escapes.
- Environment reproducibility over time is uncertain: Dependency drift, external services, nondeterministic builds, and changes in package availability may affect whether generated environments remain executable.
- The scope of supported functionality is unclear: The method is described mainly for public interfaces, CLI tools, pure functions, and stateful APIs; its effectiveness for graphical interfaces, distributed systems, concurrency, hardware-dependent code, networking, or nondeterministic applications is not demonstrated.
- Non-functional software requirements are largely omitted: Tests focus on observable functional behavior and do not establish whether generated tasks adequately assess performance, memory use, security, maintainability, concurrency correctness, or usability.
- Reference execution may encode undesirable implementation behavior: Using the original implementation to generate expected outputs can reproduce undocumented bugs, accidental behaviors, or security vulnerabilities rather than the intended specification.
- The treatment of ambiguous or underspecified behavior is not evaluated: The paper does not measure how often agents identify ambiguity, how reviewers resolve it, or whether different valid interpretations lead to inconsistent task labels.
- Leakage filtering may not detect all solution shortcuts: The adversarial rollout procedure could miss semantic leakage through dependency metadata, package versions, generated documentation, APIs, commit history, or indirect behavioral oracles.
- The relationship between verifier-passing and real software quality remains unknown: Passing synthesized tests may not imply robust, maintainable, secure, or production-ready code.
- The behavioral analyses are correlational: The association between self-verification and higher pass rates does not show that self-verification causes success; more capable rollouts may simply both verify more and solve more often.
- Behavioral metrics have limited construct validity: Counts of read/search calls, drafting overlap, and distinct verification commands may not accurately measure exploration, reasoning quality, or verification effectiveness.
- The changing rollout populations complicate behavioral comparisons: The measured subsets can vary across checkpoints, and the paper does not fully control for task difficulty, truncation, failed runs, or interaction-budget effects.
- The impact of longer trajectories is ambiguous: Increased token or interaction length may reflect productive reasoning, inefficient exploration, or reward-hacking behavior; the paper does not distinguish among these possibilities.
- The analysis does not investigate failure modes in depth: There is little characterization of incorrect solutions, such as specification misunderstanding, incomplete exploration, brittle patches, test overfitting, dependency errors, or environment failures.
- Long-term and iterative software-engineering performance is untested: The evaluations primarily measure completion of isolated tasks and do not assess maintenance over successive changes, regression prevention, or performance across extended project lifecycles.
- Human and expert comparisons are absent: The paper does not compare CodeMidas-generated task difficulty, verifier quality, or agent solutions with human-authored tasks, human developers, or expert-reviewed patches.
- The cost-effectiveness of agentic environment construction is not reported: Compute, time, human-review effort, storage, and containerization costs are not compared with those of existing task-generation pipelines.
- The public reproducibility of the dataset and pipeline is unclear: The paper does not specify whether all repositories, task statements, containers, verifiers, filtering traces, and training configurations will be released sufficiently for independent replication.**
Practical Applications
Immediate Applications
The paper’s strongest near-term applications concern software engineering, developer tooling, and research infrastructure. They are deployable now using containerized execution, open-source repositories, automated test generation, and existing coding-agent models.
- Automated coding-agent training for software companies (software engineering; industry)
- bug fixing and issue resolution;
- feature implementation;
- code translation between languages;
- command-line and terminal operations;
- repository-level software maintenance.
- A practical product would be an internal pipeline that selects a public API or command-line feature, removes its implementation, generates a behavioral specification and hidden tests, and uses the resulting environment for agent fine-tuning.
- Repository-specific coding copilots (developer tools; enterprise software)
- proprietary SDKs;
- infrastructure-as-code repositories;
- internal data-processing tools;
- service APIs and microservice interfaces;
- legacy systems with limited documentation.
- The agent could then assist with repository navigation, implementation, testing, and regression repair.
- Automated test and verifier generation (software quality assurance)
The execution-grounded test-construction procedure can be used independently of reinforcement learning to generate or expand test suites. A testing tool could:
- inspect a public interface;
- execute a trusted reference implementation;
- generate input-output and state-transition cases;
- remove assertions that encode undocumented implementation details;
- run the tests against alternative implementations. This can help identify missing edge cases, weak tests, and false-positive “solved” patches.
Pre-deployment evaluation of coding agents (AI safety; software engineering)
- genuinely implements requested behavior rather than retrieving hidden artifacts;
- explores the relevant codebase;
- performs self-verification;
- succeeds across diverse languages and repository structures;
- produces correct implementations accepted by behavior-based tests.
- This is particularly useful for procurement, model release evaluation, and regression testing between agent versions.
- Agent-assisted code review and patch validation (software maintenance; cybersecurity)
- explicit requirements;
- expected public behavior;
- boundary cases;
- stateful call sequences;
- compatibility with existing dependencies.
- This is useful for detecting patches that pass shallow test suites but violate requirements, an issue the paper explicitly addresses through verifier review and post-rollout filtering.
- Interactive developer workflows emphasizing exploration and self-checking (daily professional use)
- repository search before editing;
- inspection of callers and dependencies;
- drafting before making changes;
- multiple forms of self-verification after edits.
- IDE plugins and terminal agents could expose automated checklists or prompts such as “inspect usages,” “run targeted tests,” and “test boundary conditions.” This is an immediate workflow improvement even without retraining a new model.
- Scalable academic benchmarks for software engineering agents (academia)
- programming language;
- task difficulty;
- number of affected files;
- interface type;
- stateful versus stateless behavior;
- test strength and specification completeness.
- The fail-to-pass requirement and repeated execution checks provide a reproducible basis for comparing RL methods, reward designs, exploration strategies, and verifier quality.
- Training data-quality auditing for coding RL (machine learning research)
- environment reproducibility checks;
- adversarial leakage scans;
- verifier–specification agreement reviews;
- selection of tasks with both successful and failed rollouts;
- deduplication across repositories and functionality.
- This can reduce wasted compute and prevent models from learning exploitable shortcuts.
- Open-source maintenance assistance (open-source communities; public-sector software) Maintainers can deploy coding agents trained or evaluated with source-derived environments to handle routine tasks such as API additions, compatibility fixes, refactoring, and test creation. Human maintainers would still approve patches, while the agent handles implementation and produces evidence from executable checks.
- Potentially relevant non-software sectors through domain-specific repositories (engineering, finance, healthcare, energy)
- scientific computing and simulation;
- financial analytics libraries;
- healthcare data-processing software;
- energy-system modeling tools;
- robotics middleware and control libraries.
- In these domains, the immediate use should generally be developer assistance and testing, not autonomous operation of safety-critical systems.
Long-Term Applications
The longer-term opportunities depend on scaling the method, improving verifier reliability, addressing intellectual-property and privacy constraints, and validating whether benchmark gains translate into dependable real-world deployment.
- Autonomous software engineering teams (enterprise software; long-term)
- stronger multi-step and process-level rewards;
- reliable handling of ambiguous requirements;
- secure access to production-like environments;
- human escalation mechanisms;
- evaluation on real organizational outcomes rather than benchmark pass rates alone.
- Continuous self-improvement of coding agents from live repositories (AI infrastructure; long-term)
- automatic curriculum construction;
- language- and domain-balanced sampling;
- difficulty progression;
- targeted training on recurring agent failures;
- rapid adaptation to new frameworks and APIs.
- Feasibility depends on preventing data leakage between training and evaluation and on establishing legal authorization for repository use.
- Agent training for robotics, embedded systems, and cyber-physical software (robotics; embedded systems; long-term)
- hardware-in-the-loop verification;
- timing, resource, and safety constraints;
- deterministic simulators;
- safeguards against unsafe code;
- validation under sensor noise and hardware variability.
- High-assurance software development in healthcare, finance, and energy (regulated industries; long-term)
- formal methods and property-based testing;
- traceable requirements;
- independent verification and validation;
- regulatory documentation;
- security and privacy review;
- guarantees for rare and high-impact failure modes.
- Policy and public-sector auditing of AI-generated software (policy; government procurement; long-term)
- reproducible execution;
- resistance to artifact leakage;
- behavioral rather than implementation-specific testing;
- performance across representative repositories;
- independent verifier review;
- documented uncertainty and failure rates.
- This could inform procurement rules for AI-assisted software development and public-sector cybersecurity controls.
- Automated migration and modernization of legacy systems (enterprise IT; long-term)
- preservation of undocumented behavior;
- compatibility with external systems;
- long-duration regression testing;
- performance equivalence;
- rollback and audit capabilities.
- Personalized programming education and adaptive training environments (education; long-term)
- realistic multi-file programming assignments;
- feedback based on executable behavior;
- practice in debugging and terminal use;
- individualized curricula based on failure patterns;
- training in testing and codebase exploration.
- Deployment depends on filtering licensing-sensitive code, avoiding overly difficult or ambiguous tasks, and ensuring that generated tests assess learning objectives rather than accidental implementation details.
- Benchmarking and training agents for general computer use beyond coding (AI research; long-term) The central design principle—turning observable behavior in an existing system into an executable task and verifier—could extend to configuration management, data workflows, scientific tools, and enterprise applications. This would require reliable environment snapshots, safe reset mechanisms, and verifiers for outcomes that are not naturally represented as unit-test pass/fail results.
- Large-scale software ecosystem intelligence (industry analytics; long-term)
- prioritize documentation;
- improve API design;
- identify fragile components;
- forecast maintenance costs;
- guide investments in testing infrastructure.
- Such analysis must account for repository-selection bias, language imbalance, and the fact that the paper’s dataset is concentrated in Python, TypeScript, Go, C++, and JavaScript, with systems software, web technologies, and developer tools especially prominent.
- General-purpose autonomous agents with learned verification habits (long-term AI systems) The observed increases in repository exploration, code drafting, and diverse self-verification suggest a broader research direction: training agents not merely to produce outputs, but to develop reliable work habits. A future agent could learn to inspect context, formulate hypotheses, test alternatives, and revise its work across software, data, and technical research tasks. This remains dependent on stronger causal evidence that these behaviors improve real-world reliability, rather than merely correlating with success on the paper’s evaluation environments.
Across these applications, the main feasibility assumptions are that the source code can legally be used, the original implementation provides a trustworthy behavioral reference, dependencies can be reproduced in containers, generated tests accurately reflect stated requirements, and hidden verifiers are resistant to leakage and specification gaps. The reported benchmark improvements demonstrate strong promise for coding-agent training, but they do not by themselves establish safe, unsupervised deployment in high-impact or safety-critical settings.
Glossary
- Adversarial rollout: An agent execution deliberately designed to find weaknesses or exploitable information in a task environment. “In adversarial rollouts, an agent tries to exploit residual leakage to recover a solution without doing the intended development work.”
- Agentic coding: The autonomous use of an AI agent to perform software-development tasks over multiple interactions. “LLMs are increasingly capable of agentic coding: completing substantial pieces of real software work autonomously over long horizons”
- Behavioral specification: A description of required externally observable behavior, independent of internal implementation details. “agents explore implemented functionality to formulate behavioral specifications”
- Binary execution reward: A reward consisting of one of two values, typically indicating whether execution-based tests passed or failed. “return a binary execution reward for RL.”
- Codebase adaptation: The modification of an existing software repository to create a development starting point for a task. “We first introduce task design and codebase adaptation”
- Codebase exploration: Systematic inspection of a repository’s files, structure, dependencies, and interfaces. “To characterize the behavioral changes accompanying the performance gains, we analyze codebase exploration”
- Compiled artifact: A generated binary or other output produced by compiling source code. “Cleanup removes artifacts that could reveal the deleted implementation, including compiled outputs”
- Containerized development environment: An isolated, reproducible software environment packaged with its dependencies and runtime resources. “Each task consists of a statement, a containerized development environment, and a hidden executable verifier.”
- Cross-codebase generalization: The ability of a model trained on some software repositories or tasks to perform effectively on different repositories or task types. “These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.”
- Execution consistency: The stability of expected pass or fail behavior across repeated executions in fresh environments. “We then describe how agent rollouts are used to filter environments before RL training”
- Execution-grounded test: A test whose expected behavior is derived from running a reference implementation. “We present CodeMidas, an agentic pipeline that automatically constructs executable coding RL environments using source code as its only task-specific input.”
- Execution-based evaluation: Evaluation that judges a solution by running it and observing its outputs or effects. “SWE-bench~\citep{swebench} established repository-level issue resolution as an execution-based evaluation setting.”
- Execution reward: A reinforcement-learning signal computed from whether a submitted program passes executable tests. “CodeMidas uses GRPO~\citep{grpo} with execution rewards from synthesized tests”
- Exploitable leakage: Unintended information in an environment that allows an agent to obtain the solution without implementing the requested behavior. “Post-rollout filtering checks for exploitable leakage”
- False negative: A verifier outcome in which a correct implementation is incorrectly judged to have failed. “It flags false positives when an implementation judged incorrect passes the tests, and false negatives when an implementation judged correct fails.”
- False positive: A verifier outcome in which an incorrect implementation is incorrectly judged to have passed. “It flags false positives when an implementation judged incorrect passes the tests”
- GRPO (Group Relative Policy Optimization): A reinforcement-learning method that compares sampled outputs within groups to optimize a language-model policy. “We then train MiMo-V2.5 on these tasks using GRPO”
- Held-out task: A task excluded from training and reserved for evaluating generalization. “To assess generalization beyond CodeMidas, we compare early and late checkpoints on SWE-bench Pro, ProgramBench, and Terminal-Bench v2.1”
- Hidden executable verifier: A grading program kept inaccessible to the solver and run only after implementation is submitted. “Each task consists of a statement, a containerized development environment, and a hidden executable verifier.”
- Interquartile range: The interval between the 25th and 75th percentiles of a distribution. “with an interquartile range of 66--305 lines.”
- Long-horizon task: A task requiring many sequential actions or interactions before completion. “completing substantial pieces of real software work autonomously over long horizons”
- Observable behavior: Externally detectable outputs, return values, state changes, or effects of a program. “Its public interfaces and observable behavior help define what an agent should implement”
- Post-rollout filtering: Removing task environments after inspecting agent attempts and their outcomes. “CodeMidas then applies post-rollout filtering”
- Process reward model: A learned model that evaluates intermediate actions or steps rather than only the final result. “For SWE agents, SWE-Shepherd~\citep{sweshepherd} scores actions with a process reward model”
- Reference solution: A separately retained correct implementation used to establish expected behavior or assess submitted solutions. “The original implementation is retained separately to provide a reference solution for the task.”
- Repository-level issue resolution: Repairing a software issue while considering and modifying an entire code repository rather than an isolated function. “SWE-bench~\citep{swebench} established repository-level issue resolution as an execution-based evaluation setting.”
- Rollout: One complete attempted interaction sequence in which an agent works toward solving a task. “a coding agent tries four times per task.”
- Self-verification: An agent’s use of its own tests or checks to assess whether its implementation is correct. “Trajectory analysis shows the RL-trained agent demonstrates better behaviors like increasing codebase exploration and more diverse self-verification.”
- Stateful API: An interface whose behavior depends on persistent state across multiple calls. “Supported interfaces include command-line tools, pure library functions, and stateful library APIs”
- Task-specific input: Information supplied specifically to construct a task, such as source code, issues, commits, or documentation. “using source code as its only task-specific input.”
- Test oracle: A mechanism or specification used to determine whether a program’s output is correct. “Reliable execution rewards also depend on the test oracle”
- Trajectory analysis: Examination of the ordered actions, observations, and outputs produced during an agent’s attempt. “Our trajectory analysis examines agents' exploration and self-verification during RL training”
- Verifier defect: An error in a checking mechanism that causes it to accept incorrect solutions or reject correct ones. “Tasks with identified verifier defects are rejected.”
- Whole-program construction: Building an entire executable software system rather than implementing an isolated component. “issue repair (DeepSWE + 11.7\%), whole-program construction (ProgramBench +17\%)”
- Zero-shot source-derived environment construction: Creating a task environment from existing source code without relying on additional task-specific artifacts such as issues or documentation. “CodeMidas uses source code as its only task-specific input”