Training Versatile Coding Agents in Synthetic Environments (2512.12216v1)

Abstract: Prior works on training software engineering agents have explored utilizing existing resources such as issues on GitHub repositories to construct software engineering tasks and corresponding test suites. These approaches face two key limitations: (1) their reliance on pre-existing GitHub repositories offers limited flexibility, and (2) their primary focus on issue resolution tasks restricts their applicability to the much wider variety of tasks a software engineer must handle. To overcome these challenges, we introduce SWE-Playground, a novel pipeline for generating environments and trajectories which supports the training of versatile coding agents. Unlike prior efforts, SWE-Playground synthetically generates projects and tasks from scratch with strong LLMs and agents, eliminating reliance on external data sources. This allows us to tackle a much wider variety of coding tasks, such as reproducing issues by generating unit tests and implementing libraries from scratch. We demonstrate the effectiveness of this approach on three distinct benchmarks, and results indicate that SWE-Playground produces trajectories with dense training signal, enabling agents to reach comparable performance with significantly fewer trajectories than previous works.

• The paper introduces SWE-Playground, a synthetic pipeline that enables comprehensive coding agent training across the full software engineering lifecycle.
• The paper outlines a multi-phase methodology using LLM-driven project proposals, task decomposition, and unit test generation to provide high-density reward signals.
• The experimental results demonstrate that SWE-Playground-trained agents achieve competitive performance and superior generalization with 3–5× fewer training trajectories.

Training Versatile Coding Agents Using Synthetic Environments: An Authoritative Analysis

Motivation and Problem Formulation

The landscape of software engineering agent training frameworks has predominantly leveraged real-world datasets such as GitHub repositories for both environment curation and reward specification. However, this coupling to existing data introduces significant limitations: inflexible scaling and a narrow focus on issue resolution tasks. The inability to synthetically expand task types and project varieties restricts both the breadth and granularity of agent skill acquisition. The paper "Training Versatile Coding Agents in Synthetic Environments" (2512.12216) directly challenges this paradigm by proposing a synthetic environment pipeline, SWE-Playground, designed to generate diverse, verifiable coding tasks and project repositories entirely de novo. Unlike prior works, this system enables both exhaustive coverage of the coding lifecycle—extending beyond issue resolution to unit test generation and entire library implementation—and parses the full complexity of modern software engineering workflows.

SWE-Playground: Design, Automation, and Flexibility

SWE-Playground leverages LLMs and agentic frameworks to construct projects from first principles, decomposing high-level proposals into stepwise tasks spanning several development phases. Its pipeline comprises:

1. Project Proposal: LLMs are prompted with explicit algorithmic and architectural constraints (e.g., multi-component design, CLI-only interfaces, explicit exclusion of high-level libraries), producing a set of candidate synthetic projects.

2. Task Decomposition: Each project is systematically partitioned via hierarchical decomposition (phases → modules → tasks), with unit test specification directly embedded into the workflow through checklists produced alongside each concrete task.

3. Repository Scaffolding and Test Generation: Agents instantiate core code structures and environmental files, initializing all function stubs without implementing logic. Subsequently, a dedicated agent generates rigorous unit tests based solely on documentation and task descriptions, ensuring specification-driven and implementation-agnostic coverage.

4. Functionality Implementation and Mutual Verification: Agents perform implementation under the constraint of not initially observing the provided unit tests, promoting generalization and the development of intrinsic software verification skills. Only after initial implementation do agents access the tests, iterating to compliance. Critically, the final tests are swapped in to prevent reward hacking.

This pipeline is extensible to arbitrary task formats. For example, it supports issue injection for issue resolution/reproduction (SWE-bench, SWT-Bench) by crafting and injecting bespoke bugs, and can simulate library generation from a blank template as in Commit-0.

Experimental Evaluation and Numerical Findings

SWE-Playground is evaluated through finetuning Qwen-2.5-Coder-7B and 32B models exclusively on 704 SWE-Playground-generated trajectories, compared against strong baselines (SWE-Gym, R2E-Gym, SWE-smith) which utilize much larger datasets (up to 5,000+ trajectories). Empirical assessments span three core benchmarks capturing real-world issue resolution (SWE-bench Verified), issue reproduction via test generation (SWT-Bench), and full-library construction (Commit-0).

Key numerical results:

• SWE-Playground-trained agents achieve comparable or superior generalization across all benchmarks, despite a 3–5$\times$ reduction in trajectory count compared to R2E-Gym and SWE-smith.
• On SWT-Bench and Commit-0, SWE-Playground-trained models dominate across resolved rate and coverage delta, particularly outperforming baselines on out-of-distribution scenarios—a sharp contrast with agents overfitted to SWE-bench.
• Trajectories generated by SWE-Playground exhibit much higher "learning density": token count, assistant actions, tool invocations, and bash executions per trajectory are 2–3$\times$ higher than in baseline pipelines.
• Ablation studies demonstrate that training solely on general (library construction) or issue resolution tasks is dominated by the mixed regime; inclusion of issue reproduction and library generation trajectories is essential for cross-scenario transferability.
• Performance per token and per trajectory is significantly higher, realizing competitive or superior performance at lower computational cost and reduced sample complexity.

Theoretical and Practical Implications

SWE-Playground introduces a paradigm shift for agentic software engineering datasets. By divorcing training data generation from existing, limited sources and enabling full control over project/task specification, SWE-Playground breaks the dependency bottleneck and offers several implications:

• Scalability and Diversity: Synthetic generation enables arbitrary scaling of task complexity, programming domains, and codebases, with fine-grained control over project properties. This holds potential for targeted domain specialization, adversarial robustness training, and task type balancing.
• Dense and High-Quality Reward Signals: By constructing rigorous test suites for each task, SWE-Playground overcomes the reward sparsity and ambiguity associated with mining real-world code activity, providing verifiable and implementation-independent feedback.
• Generalization and Transferability: The heterogeneous, adversarially structured trajectories serve as an effective antidote to overfitting, cultivating agents that perform robustly outside narrowly defined task templates.
• Data Efficiency: The pipeline validates the "Agency Efficiency Principle," implying that high-density, high-complexity trajectories can substitute for mass-scale, shallow datasets, with direct consequences for sustainable model training and iteration cycles.
• Automatability and Adaptability: End-to-end generation facilitates low-overhead adaptation to new coding paradigms (multimodal, multilingual, hardware-specific tasks), inviting broad testbed expansion and rapid research iteration.

Limitations and Future Directions

Several open questions and research paths are identified:

• Multimodal and Performance-Centric Environments: SWE-Playground’s methodologies could be extended to generate environments for evaluating code grounded in visual or performance constraints, pushing towards complete software engineering competence.
• Reinforcement Learning Integration: While this work focuses on supervised trajectory finetuning, an RL-based agent could, within these self-curated environments, develop even more sophisticated self-verification and error-correction policies—a step toward self-improving, self-correcting generalist coding agents.
• Verification Reliability: Although unit test-based reward signals are robust, their sufficiency for detecting all classes of subtle implementation bugs remains an open research problem and motivates further studies on test adequacy and adversarial validation.

Conclusion

The introduction of SWE-Playground constitutes a substantial advance in the construction of agentic environments for software engineering. By generating rich, diverse, and specification-driven synthetic projects, tasks, and verification signals, SWE-Playground enables the training of coding agents that not only match but often surpass models trained on much larger conventional datasets, particularly in data efficiency and cross-task generalization. The methodology is representative of a future in which training environments for coding agents become increasingly automated, tunable, and aligned with the full complexity and heterogeneity of real-world software engineering challenges (2512.12216).