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SWE-RM: Execution-free Feedback For Software Engineering Agents (2512.21919v1)

Published 26 Dec 2025 in cs.CL

Abstract: Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL). This paradigm requires scalable and reliable collection of unit test cases to provide accurate feedback, and the resulting feedback is often sparse and cannot effectively distinguish between trajectories that are both successful or both unsuccessful. In contrast, execution-free feedback from reward models can provide more fine-grained signals without depending on unit test cases. Despite this potential, execution-free feedback for realistic software engineering (SWE) agents remains underexplored. Aiming to develop versatile reward models that are effective across TTS and RL, however, we observe that two verifiers with nearly identical TTS performance can nevertheless yield very different results in RL. Intuitively, TTS primarily reflects the model's ability to select the best trajectory, but this ability does not necessarily generalize to RL. To address this limitation, we identify two additional aspects that are crucial for RL training: classification accuracy and calibration. We then conduct comprehensive controlled experiments to investigate how to train a robust reward model that performs well across these metrics. In particular, we analyze the impact of various factors such as training data scale, policy mixtures, and data source composition. Guided by these investigations, we introduce SWE-RM, an accurate and robust reward model adopting a mixture-of-experts architecture with 30B total parameters and 3B activated during inference. SWE-RM substantially improves SWE agents on both TTS and RL performance. For example, it increases the accuracy of Qwen3-Coder-Flash from 51.6% to 62.0%, and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verified using TTS, achieving new state-of-the-art performance among open-source models.

Summary

  • The paper demonstrates how execution-free feedback with calibrated, discriminative reward models leads to significantly improved RL outcomes over traditional execution-based methods.
  • The paper establishes a training protocol using a 30B-parameter mixture-of-experts model and diverse datasets, achieving state-of-the-art performance on SWE benchmarks.
  • The paper reveals that combining execution-free and execution-based signals enhances both efficiency and reliability in software engineering workflows.

Authoritative Summary of "SWE-RM: Execution-free Feedback For Software Engineering Agents" (2512.21919)

Introduction and Motivation

Contemporary coding agents built with LLMs rely on feedback mechanisms to optimize and evaluate automated software engineering (SWE) workflows. Standard paradigms have predominantly harnessed execution-based verifiers—typically unit test suites yielding sparse, binary signals—for test-time scaling (TTS) and reinforcement learning (RL). This paradigm is subject to limitations: test suite coverage is often incomplete or noisy, and pass/fail labels provide low-granularity signals that have poor discrimination among candidate solutions, limiting policy optimization under RL and constraining data utility (2512.21919). Execution-free verifiers—reward models that score solution trajectories without sandboxing or unit test execution—offer more nuanced signals but have not been systematically explored in SWE agent optimization.

Limitations of Execution-Based Feedback and Metric Analysis

The work provides rigorous critique of relying exclusively on TTS metrics for reward model selection in RL settings. TTS measures the instance-wise ability of verifiers to rank correct solutions first, but empirical results show that verifiers with similar TTS metrics can yield drastically different RL outcomes. The authors establish that TTS omits two critical axes: discriminative ability (measured by AUC) and calibration (measured by Expected Calibration Error, ECE). Poor discriminative performance (low AUC) results in reward misassignments across resolved/unresolved code patches, while poor calibration (high ECE) results in systematic bias and elevated variance in RL optimization dynamics (2512.21919). The analysis formally links TTS, AUC, and ECE to distinct RL failure modes via gradient decomposition.

SWE-RM Training Protocols and Experimental Findings

SWE-RM is instantiated as a mixture-of-experts (MoE) LLM verifier with 30B parameters (3B active). The training procedure reformulates reward modeling as generative classification over endpoint tokens (YES/NO) and employs supervised fine-tuning over a diverse mixture of resources (SWE-Gym, SWE-rebench, SWE-smith, R2E-Gym), sampling trajectories from multiple policy models (Qwen3-Coder, Claude). The authors conduct extensive ablations:

  • Data Scaling: Increasing the scale from 5k to 100k samples monotonically improves AUC and calibration, with diminishing returns past 25k samples. Under-trained reward models have poor OOD generalization, adversely affecting both TTS and RL.
  • Positive/Negative Ratio: A 2:1 ratio yields highest AUC, lowest ECE, and elevated TTS resolve rates, outperforming more balanced ratios and promoting data efficiency.
  • Policy and Source Mixing: Mixing on-policy and off-policy data, and diverse data sources, provide robustness against overfitting and domain bias. Hybrid sourcing (primarily SWE-rebench with auxiliary SWE-smith/Gym) optimizes generalization and score calibration.
  • Context Length: Expanding the context window to 256k tokens ensures near-complete coverage of complex multi-turn trajectories, mitigating the "no-score" bottleneck that affects long-context software tasks.

Numerical Results and Contradictory Evidence

SWE-RM exhibits strong, quantifiable performance gains:

  • Qwen3-Coder-Flash: Accuracy improves from 51.6% to 62.0%.
  • Qwen3-Coder-Max: Accuracy elevates from 67.0% to 74.6%—establishing new open-source state-of-the-art on SWE-Bench Verified (2512.21919).
  • RL Training: Using SWE-RM feedback increases the pass@1 metric by 3 absolute points compared to execution-based baselines, and RL learning curves demonstrate faster, smoother convergence.

A noteworthy empirical finding is the demonstration that reward models with nearly identical TTS can produce dramatically different RL outcomes—a claim that challenges the prevailing reliance on TTS for reward model selection. Calibration and discriminative ability are shown to be mandatory for reliable RL optimization in SWE agents.

Implications and Theoretical Impact

Practical Implications

SWE-RM’s framework enables granular, execution-free reward modeling compatible with long-context coding agents (256k tokens), thus unblocking the deployment of sophisticated agentic workflows in real software engineering environments. Hybrid feedback—combining execution-free and execution-based signals—achieves best RL performance, balancing efficiency (continuous signals) with reliability (verifiable correctness).

Theoretical Implications

The work advances a more nuanced understanding of reward modeling beyond TTS. Its formal mapping between (TTS, AUC, ECE) and RL gradient dynamics provides actionable design criteria for future reward model development. Calibration emerges as a key property for stable RL optimization, suggesting that calibration-aware RM training should be a standard in future coding agent pipelines.

Future Directions

The results motivate further research in:

  • Reward model architectures synergistic with multi-agent or tool-augmented systems.
  • Datasets for trajectory-level reward modeling in broader SWE domains beyond patch fixing.
  • Automated calibration correction schemes during reward model training.
  • RL algorithms robust to miscalibrated or low-discrimination reward signals.
  • Long-context modeling (>>256k) for system-level software engineering tasks.

Conclusion

The SWE-RM verifier defines a new paradigm for feedback in SWE agent optimization, showing that TTS metrics alone are insufficient proxies for RL utility. Calibrated, discriminative, and versatile reward models dramatically improve both TTS and RL performance. The work establishes execution-free feedback as a scalable alternative to execution-based supervision and sets a methodological baseline for future research on RL-augmented code agent training (2512.21919).

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What is this paper about?

This paper is about teaching AI coding “agents” to write and fix software better by giving them smarter feedback. Instead of only using pass/fail test results (like unit tests), the authors build a “reward model” called SWE-RM that can judge code attempts without running them, give a fine-grained score, and work well both when picking the best answer at test time and when training the agent with reinforcement learning.

What questions are the researchers asking?

They focus on three simple questions:

  • When we use a judge model (a “reward model”) to score code attempts, what makes it truly good for training coding agents, not just for choosing the best of many tries?
  • Is the common score used today—test-time scaling (TTS), which measures how well a judge picks the best attempt out of many—enough to predict success during training?
  • How should we train a better judge: how much data do we need, which kinds, what balance of good/bad examples, and how long a context window?

Their main discovery: two judges with almost the same TTS score can behave very differently during reinforcement learning (RL). So TTS alone is not enough. They show two extra qualities matter a lot:

  • Discrimination (measured by AUC): how well the judge separates good and bad attempts overall.
  • Calibration (measured by ECE): how trustworthy the judge’s confidence scores are.

How did they study it?

Two kinds of feedback for coding agents

  • Execution-based feedback: Run the code against unit tests and mark pass/fail. This is precise but often “sparse” (just 0/1), needs good tests, and can’t tell how close a wrong attempt is.
  • Execution-free feedback (reward models): A model reads the entire attempt (the “trajectory”) and outputs a continuous score without running the code. This can be more detailed but must be trained to be accurate and reliable.

Think of execution-based as a red/green traffic light, and execution-free as a judge giving a score from 0 to 100.

How their reward model works

They train a large “judge” (SWE-RM) to read the whole coding attempt and then output a special token meaning YES (resolved) or NO (unresolved). From the model’s confidence in YES vs NO, they compute a score r between 0 and 1:

r=eℓYESeℓYES+eℓNOr = \frac{e^{\ell_{\text{YES}}}}{e^{\ell_{\text{YES}}} + e^{\ell_{\text{NO}}}}

This turns the judge’s belief into a smooth score (like a certainty meter).

They use a Mixture-of-Experts (MoE) model: 30 billion total parameters, but only 3 billion are active at a time during scoring, which makes it efficient.

They also support very long inputs: up to 256k tokens of context, so the judge can read long, realistic coding sessions.

How they tested what matters

They compare three judge qualities:

  • Test-time scaling (TTS): If you generate several solutions, does the judge pick the best one on top?
  • Discrimination (AUC): Across lots of attempts, how well does the judge separate correct from incorrect?
  • Calibration (ECE): If the judge says “I’m 90% confident,” is it actually right about 90% of the time?

They show examples where two judges have similarly good TTS but very different AUC and calibration—and only the better-calibrated, more discriminative judge helps RL training succeed.

What they varied during training

To learn what builds a strong judge, they ran controlled experiments changing:

  • Data scale: training on a few thousand vs tens of thousands vs ~100k attempts.
  • Positive/negative balance: the ratio of solved attempts to unsolved ones (they found about 2:1 positives:negatives worked best).
  • Policy mix: collecting attempts from different code generators (on-policy and off-policy) to diversify training data.
  • Data sources: mixing datasets (SWE-rebench, SWE-Gym, SWE-smith, R2E-Gym).
  • Context length: moving beyond 32k to 128k–256k so almost all real attempts fit.

What did they find?

Here are the main results, summarized:

  • TTS alone is not enough: Two judges with almost the same TTS can lead to very different RL outcomes. Good discrimination (higher AUC) and good calibration (lower ECE) are crucial for stable and effective training.
  • Data scale helps a lot: More training data creates clearer separation between good/bad attempts and much better calibration. Going from tiny datasets to ~100k examples sharply reduces calibration error.
  • Best label balance: Around a 2:1 ratio of positive (solved) to negative (unsolved) examples tends to produce the best overall judge quality and TTS.
  • Longer context is important: With short context (e.g., 32k), many real coding attempts are too long to score. At 128k–256k, nearly all can be scored, and performance improves.
  • Mixed policies and sources generalize better: Combining data from multiple code generators and datasets improves AUC and calibration more consistently than relying on a single source.
  • Strong practical gains:
    • With SWE-RM as the judge during test-time scaling, accuracy on the SWE-Bench Verified benchmark improved notably:
    • Qwen3-Coder-Flash: from 51.6% to 62.0%
    • Qwen3-Coder-Max: from 67.0% to 74.6%
    • During reinforcement learning, using SWE-RM as part of the reward improves final performance by about 3 percentage points over execution-based-only feedback and makes learning faster and smoother.
    • The best RL setup is “hybrid feedback”: combine execution-free scores (continuous, detailed) with execution-based signals (trustworthy pass/fail). This balances speed and reliability.

Why does it matter?

In real software engineering, unit tests can be incomplete, noisy, or missing. If we only use pass/fail signals, training AI agents is slow and brittle. This work shows that:

  • A well-built execution-free judge can give helpful, fine-grained feedback without running code, making training faster and more informative.
  • To make that judge truly useful for training (not just for picking answers), it must be both discriminative (good at telling good from bad across the board) and calibrated (confidence matches reality).
  • With careful training—enough diverse data, a good positive/negative balance, and long context—these judges can push coding agents to new state-of-the-art results.
  • In practice, combining execution-free and execution-based feedback gives the best of both worlds: continuous guidance plus reliable checks.

Big picture: Better “judges” for code attempts can make AI coding agents more accurate, faster to train, and less dependent on perfect test suites—bringing us closer to dependable AI helpers for real-world software development.

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a concise, actionable list of what the paper leaves missing, uncertain, or unexplored.

  • Quantify and mitigate training-label noise: the RM is trained using fail2pass execution labels that are known to be noisy and sometimes unrelated to issues; measure label error rates, their impact on AUC/ECE/RL, and test noise-robust training (e.g., confident learning, bootstrapping, label smoothing, or relabeling via human verification).
  • Formalize the causal link between AUC/ECE and RL outcomes: provide controlled studies and theory connecting these metrics to RL stability and performance (beyond Appendix C), including sensitivity analyses and ablations that hold TTS constant while varying calibration/discriminative power.
  • Calibration methods are not compared: evaluate post-hoc calibration techniques (temperature scaling, isotonic regression, Platt scaling, Dirichlet calibration) and in-training calibration losses; quantify effects on RL stability and final pass@1.
  • Reward hacking risk is unaddressed: test whether agents learn to exploit RM scoring without true resolution (e.g., adversarial or cosmetic patches); build adversarial datasets, run red-teaming, and propose defenses (e.g., adversarial training, consistency checks, anti-gaming regularizers).
  • Hybrid reward weighting is ad-hoc: systematically ablate the constants in Eq. (1), try learned weighting (bandits, meta-learning), normalization/clipping, uncertainty-aware mixing, and per-task adaptive schedules; report sensitivity and optimal configurations.
  • Alternative RM objectives/architectures are unexplored: compare generative classification with pairwise/listwise ranking, contrastive learning, step-level/patch-level critics, regression on pass probability, or joint trajectory–patch encoders; measure effects on TTS/AUC/ECE and RL.
  • Patch vs trajectory contribution is not disentangled: ablate scoring on patch-only, trajectory-only, and combined inputs to determine minimal sufficient context and where most signal arises; guide cheaper scoring pipelines.
  • Context-length trade-offs lack cost analysis: quantify memory/latency/throughput for 128k–256k sequences; evaluate streaming, summarization, or selective attention to reduce cost while preserving RM quality.
  • RL setup is restricted: assess multiple rollouts, non-greedy decoding, test-time scaling within RL, curriculum learning, advantage normalization, and alternative algorithms (PPO variants, offline RL); report sample efficiency and stability across settings.
  • Generalization across base models and scaffolds in RL is limited: run RL with diverse code models (e.g., GLM, DeepSeek, Kimi) and agent frameworks/tools beyond OpenHands; measure transferability of SWE-RM’s benefits.
  • Cross-benchmark contamination/decontamination is unclear: verify and report whether training includes tasks overlapping with evaluation (including SWE-Bench Verified); release decontamination protocols and audits.
  • Data composition granularity is missing: analyze per-repository/per-language/per-difficulty performance, source-specific calibration differences, and how source mixtures affect OOD generalization; use stratified reporting.
  • Positive-to-negative ratio is bounded by data scarcity: explore generating or mining additional positives (e.g., human-verified, mined from accepted PRs, synthetic from high-confidence RM) to test ratios beyond 2:1 and their effects on calibration/AUC/RL.
  • Thresholding policies are not optimized: study precision–recall trade-offs, per-task thresholds, and dynamic acceptance criteria; connect thresholds to agent decision-making (e.g., early stop, patch submission gating).
  • Multilingual and non-Python coverage is sparse: expand evaluation to more languages, build language-specific calibration analyses, and test RM behavior on ecosystem-specific tooling and build systems.
  • Safety and semantic correctness beyond unit tests are not addressed: add semantic validators (static analysis, security scanners), side-effect detection, and human code review signals; quantify improvements over test-only correctness.
  • Efficiency and deployment concerns are unquantified: report training/inference cost for the 30B MoE (3B active) with 256k context, latency under production constraints, and strategies for distillation/pruning/quantization without losing calibration.
  • RM release and reproducibility are incomplete: provide code, weights, training recipes, seeds, evaluation scripts, and standardized pipelines to reproduce figures (e.g., Figure 1/2/3/4/5/7) and tables; include variance/CI across runs.
  • Online calibration drift is unstudied: track ECE/AUC as the policy evolves during RL, evaluate online recalibration or periodic RM updates, and study interactions between RM updates and RL stability.
  • Metric set may be incomplete: assess PR-AUC, Brier score, NLL, decision cost curves, AUROC by strata, and calibration under distribution shift; link metric changes to concrete RL improvements.
  • OOD detection is missing: add uncertainty estimation (e.g., ensembles, MC dropout) or novelty detection for trajectories to avoid overconfident scoring on rare patterns; integrate uncertainty into reward shaping.
  • Evaluation on noisy vs verified test suites: quantify SWE-RM sensitivity to test noise in non-verified benchmarks; assess whether hybrid feedback remains robust when execution signals are unreliable.
  • Impact of policy mixture breadth is limited: expand off-policy data beyond Claude to other families (e.g., Llama, DeepSeek, GLM) and measure how diversity affects RM generalization/calibration and RL outcomes.
  • Theoretical analysis details are unavailable: publish full proofs, assumptions, and empirical validations connecting TTS/AUC/ECE to RL convergence properties; specify conditions where each metric dominates.
  • Ethical/legal considerations are not discussed: clarify repository licensing, data usage compliance, and privacy concerns in training and evaluation; propose guidelines for responsible deployment in real-world repos.
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Glossary

  • Ablation study: An experimental method that systematically varies or removes components to assess their impact on performance. "we conduct large-scale ablation studies examining the effects of training data scale, the ratio of positive to negative samples, mixtures of data sources, and context length."
  • Agentic reinforcement learning: Reinforcement learning where models act as interactive agents over multi-turn trajectories and tools. "which enables efficient multi-turn agentic reinforcement learning and SGLang for trajectory rollout."
  • Area Under the ROC Curve (AUC): A ranking metric measuring how well a verifier discriminates between correct and incorrect trajectories across thresholds. "AUC (Bradley, 1997) evaluates discriminative ability by measuring how well the verifier separates resolved from unresolved trajectories across the entire distribution"
  • Calibration: The degree to which a model’s confidence scores match empirical correctness. "Calibration (Wang, 2025) quantifies the alignment between predicted confidence scores and empirical correctness, for example by using Expected Calibration Error (ECE) (Guo et al., 2017)."
  • Context length: The maximum number of tokens a model can ingest in one input, affecting ability to score long trajectories. "our execution-free verifiers are the first to scale up to 256k context length, enabling the scoring of complex and long trajectories."
  • Execution-based feedback: Reward or verification derived from running code or unit tests and observing pass/fail outcomes. "Execution-based feedback like unit testing is widely used in the development of coding agents through test-time scaling (TTS) and reinforcement learning (RL)."
  • Execution-free feedback: Model-based scoring that provides continuous signals without running code or tests. "execution-free feedback from reward models can provide more fine-grained signals without depending on unit test cases."
  • Expected Calibration Error (ECE): A scalar metric quantifying mismatch between predicted confidence and actual accuracy. "ECE (Guo et al., 2017), which measures calibration representing whether the verifier's scores align with empirical correctness."
  • Fail2pass test: A unit-test-based check where a patch is judged by whether previously failing tests are turned to passing. "These trajectories are then labeled as positive or negative based on their execution results with the provided fail2pass test."
  • Generative classification: A setup where a generative model outputs special class tokens (e.g., YES/NO) to classify inputs. "we formulate reward modeling as a generative classification task, where the reward model takes a trajectory as input and outputs a special token (e.g., YES/NO)."
  • Greedy decoding: An inference strategy selecting the highest-probability token at each step without search. "we only generate 1 trajectory and 1 patch for each instance using greedy decoding following OpenHands (Wang et al., 2025), without any test-time scaling, as the final pass@1 score."
  • Group Sequence Policy Optimization (GSPO): An RL algorithm designed to improve stability, especially for Mixture-of-Experts training. "We adapt GSPO (Zheng et al., 2025) which provides greater stability for Mixture-of-Experts RL training"
  • Hybrid feedback: A combination of execution-free and execution-based signals used jointly during RL. "Hybrid feedback, where the feedback is a combination of execution-free (SWE-RM) and execution-based signals, as will be defined in Eq. 1"
  • Mixture-of-experts (MoE): A neural architecture that routes inputs to a subset of expert subnetworks, enabling sparse activation. "adopting a mixture-of-experts architecture with 30B total parameters and 3B activated during inference."
  • Off-policy data: Training data collected from a different policy/model than the one being trained. "for off-policy data we sample using Claude-sonnet-4 (Anthropic)."
  • On-policy data: Training data collected from the same policy/model that is being trained. "For on-policy data, we sample training examples using the corresponding Flash/Max model on SWE-rebench"
  • Out-of-domain (OOD): Data drawn from distributions not represented (or underrepresented) in training, stressing generalization. "Poorly trained reward models often exhibit unexpected behavior when evaluated on out-of-domain (OOD) data."
  • Pass@1: The probability or rate that the first generated attempt solves the task. "without any test-time scaling, as the final pass@1 score."
  • Reliability diagram: A plot of predicted confidence versus empirical accuracy used to visualize calibration. "Figure 1: Reliability diagrams for verifier A (left) and verifier B (right) with similar TTS performance."
  • Resolve rate: The fraction of tasks/instances successfully resolved by a model or selection procedure. "SWE-Bench Verified Resolve Rate (%)"
  • Reward model (RM): A model that scores trajectories to provide reward signals or verification without execution. "Throughout this paper, we will use 'reward model' and 'execution-free verifier' interchangeably."
  • Reward shaping: Adjusting or augmenting reward signals to guide learning dynamics and improve efficiency. "indicating effective reward shaping."
  • RM@K: The resolve rate of the final selected trajectory from k sampled candidates according to the reward model. "RM@K is defined as the resolve rate of the final selected trajectories from k samples."
  • Sandbox environments: Isolated execution contexts used to safely run code and tests. "provide a continuous score without sandbox environments."
  • Test-time scaling (TTS): Improving performance by generating multiple candidates at inference and selecting the best ranked by a verifier. "it is straightforward to adopt TTS (e.g., best of k) performance directly as the metric to guide the reward model training"
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Practical Applications

Immediate Applications

These applications can be deployed now using the paper’s methods, metrics, and release-ready artifacts (e.g., SWE-RM, hybrid feedback recipes, evaluation toolkit).

  • Industry (Software): CI/CD merge gating with a calibrated execution-free critic
    • Use case: Score and rank k candidate patches (from agent-based or human submissions) and gate merges using hybrid signals (unit tests + SWE-RM score).
    • Sector: Software, DevOps
    • Tools/products/workflows: A “SWE-RM Critic” GitHub Action/Jenkins plugin; reliability-threshold gating (set by ECE-calibrated scores); dynamic test prioritization (run expensive CI only if RM score exceeds threshold).
    • Assumptions/dependencies: Sufficient compute for 3B-activated MoE inference; repository/domain similarity to training distribution; availability of at least basic unit tests for hybrid feedback; long-context support (128k–256k) if trajectories are large; careful threshold selection guided by calibration curves.
  • Industry (Software QA/Test): Augment sparse or low-quality unit tests with fine-grained scoring
    • Use case: Replace or supplement binary pass/fail with a continuous “resolve likelihood” score for issue-fixing and program repair tasks, especially in repos with limited test coverage.
    • Sector: Software Quality Assurance
    • Tools/products/workflows: Patch scoring dashboards; score-based triage queues; reviewer assist bots highlighting near-miss trajectories using AUC-informed separation.
    • Assumptions/dependencies: Reward model should be adapted or fine-tuned to local codebases; ensure calibration monitoring (ECE, reliability diagrams) to prevent overconfidence-driven misgates.
  • Industry/Academia: Sample-efficient RL training for coding agents via hybrid feedback
    • Use case: Integrate execution-free feedback (SWE-RM) with execution-based signals to accelerate and stabilize RL for agentic coders (observed +3 absolute pass@1 on SWE-Bench Verified).
    • Sector: Software, AI tooling
    • Tools/products/workflows: Adopt the paper’s GSPO RL recipe; use OpenHands scaffold; apply Eq. (1) hybrid reward shaping; warm-start on Qwen3-30B-A3B.
    • Assumptions/dependencies: RL infra (Megatron, SGLang, verl) and sandboxing; compute budget; test suite noise mitigation; data privacy/governance for internal trajectory collection.
  • Academia: Multi-metric verifier evaluation and reporting (TTS, AUC, ECE)
    • Use case: Replace TTS-only evaluation with the proposed triad (TTS for top-1 ranking, AUC for discrimination, ECE for calibration), improving scientific rigor and reproducibility.
    • Sector: Research, benchmarking
    • Tools/products/workflows: Reliability diagrams; score distribution audits; standardized RM@k reporting; controlled ablations on data scale, positive/negative ratios (suggested 2:1), policy mixtures, and source composition.
    • Assumptions/dependencies: Access to trajectory datasets; consistent labeling (fail2pass outcomes); publish code to generate ROC/ECE plots.
  • Industry (Developer Experience): IDE “critic mode” for patch suggestions
    • Use case: Developer assistants that present multiple patch candidates and rank them by calibrated resolution likelihood, flagging partial fixes and risky changes.
    • Sector: Software, developer tooling
    • Tools/products/workflows: VS Code/JetBrains plugins; inline reliability cues; per-patch score plus confidence calibration; threshold knobs for conservative vs aggressive adoption.
    • Assumptions/dependencies: On-device or server-side inference latency targets; domain adaptation to specific stacks (language/framework); human-in-the-loop validation.
  • Education: Auto-grading and formative feedback where full test coverage is unavailable
    • Use case: Grade student bug-fixing assignments using continuous scores; provide fine-grained guidance on near-correct trajectories.
    • Sector: Education/EdTech
    • Tools/products/workflows: Grading dashboards with reliability diagrams; rubric mapping from scores to feedback; cohort-wide calibration checks.
    • Assumptions/dependencies: Curriculum-specific tuning (domain/context); guardrails for miscalibration; transparent scoring explanations for fairness.
  • Security/Compliance: Risk-based vulnerability patch vetting
    • Use case: Prioritize and gate security patches using hybrid signals for higher trust while reducing manual review load.
    • Sector: Security, regulated software
    • Tools/products/workflows: Vulnerability triage pipelines; thresholds calibrated to risk tolerance; audit logs of score distributions and AUC/ECE metrics per campaign.
    • Assumptions/dependencies: Domain-specific re-training; strict logging and explainability for audits; conservative thresholds to mitigate false positives.
  • Data/ML Engineering: Guardrails for agent-modified data pipelines and MLOps configs
    • Use case: Score agent-suggested changes to DAGs/configs before applying; route low-confidence proposals to manual review.
    • Sector: Data Engineering, MLOps
    • Tools/products/workflows: Pre-merge RM scoring; hybrid gating with smoke tests; reliability dashboards.
    • Assumptions/dependencies: Domain adaptation (pipelines may differ from SWE-Bench tasks); long-context inputs; clear rollback procedures.
  • Dataset Curation: Practical recipe for collecting better verifier training data
    • Use case: Apply the paper’s findings to construct high-quality RM training corpora (mix policy, multi-source, 2:1 positive:negative ratio, scale to ~100k samples).
    • Sector: Data curation for AI
    • Tools/products/workflows: Policy mixtures (on-policy + off-policy); source blending (SWE-rebench primary, SWE-smith + SWE-Gym for calibration); context-length planning.
    • Assumptions/dependencies: Access to agent trajectories; legal and privacy constraints; compute for long-context processing.

Long-Term Applications

These applications will benefit from further research, scaling, and engineering (e.g., domain adaptation, reliability guarantees, standards).

  • Industry: RM-as-a-Service and standardized APIs for enterprise codebases
    • Use case: Offer hosted execution-free verifier endpoints with 256k-context support for monorepos/microservices, integrated into SCM and CI/CD.
    • Sector: Software, cloud services
    • Tools/products/workflows: Managed services with SLA; per-repo calibration profiles; cross-language scoring; cost-aware batching.
    • Assumptions/dependencies: Privacy and IP controls; robust domain generalization; cost optimization for large contexts; compliance certifications.
  • Autonomous Dev Platforms: End-to-end agentic issue resolution at scale
    • Use case: Platforms that continuously fix issues with hybrid reward shaping and calibrated critics, escalating edge cases to humans.
    • Sector: Software, DevOps
    • Tools/products/workflows: Closed-loop RL training with on-the-fly data collection; human-in-the-loop governance; adaptive thresholds based on reliability diagrams.
    • Assumptions/dependencies: Safety policies; organizational change management; standardized metrics (AUC/ECE) as go/no-go gates; long-horizon task orchestration.
  • Test Generation Optimization: RM-guided unit test synthesis
    • Use case: Use RM score overlaps/miscalibration hotspots to target new unit tests that improve discriminability and reduce reward noise.
    • Sector: Software QA/Test generation
    • Tools/products/workflows: Active test generation targeting ambiguous trajectories; feedback loops to reduce ECE; incremental coverage expansion.
    • Assumptions/dependencies: Algorithms linking score patterns to test design; compute to iterate; evaluation pipelines for coverage impact.
  • Cross-Domain Reward Models: Execution-free feedback for agents beyond SWE
    • Use case: Calibrated reward models for domains where execution is costly/unsafe or labels are noisy (e.g., robotics task plans, clinical workflow suggestions, financial model changes).
    • Sector: Robotics, healthcare, finance, energy
    • Tools/products/workflows: Domain-specific trajectory collection and labeling; AUC/ECE-centric evaluation; hybrid feedback (e.g., simulations + RM scores).
    • Assumptions/dependencies: High-stakes safety/regulatory constraints; significant domain re-training; trustworthy simulators for hybrid signals; rigorous calibration procedures.
  • Policy/Standards: Calibration and discrimination reporting for AI dev tools
    • Use case: Regulatory or industry standards requiring verifier tools to report calibration (ECE), discrimination (AUC), and reliability diagrams alongside TTS metrics.
    • Sector: Policy, compliance, procurement
    • Tools/products/workflows: Certification frameworks; audit-friendly reporting formats; minimum thresholds for deployment.
    • Assumptions/dependencies: Stakeholder consensus; third-party benchmarking; ongoing monitoring requirements.
  • Tooling for Monorepo/Legacy Systems: 256k-context critics for complex codebases
    • Use case: Long-context verification for large, legacy systems spanning multiple repositories and languages.
    • Sector: Enterprise software
    • Tools/products/workflows: Memory-optimized inference; chunking and context stitching; cross-repo semantic linking.
    • Assumptions/dependencies: Hardware scaling; model improvements to handle extreme contexts reliably; careful prompt engineering for trajectory + patch inputs.
  • Academic Research: Foundations for RL with verifiable reward in long-horizon tasks
    • Use case: Extend the paper’s evaluation triad and training recipes to multi-step, tool-using agents; study how calibration affects policy stability.
    • Sector: AI research
    • Tools/products/workflows: Open datasets of trajectories; standardized ablation protocols (data scale, ratios, policy mixtures); theoretical analysis linking AUC/ECE to RL dynamics.
    • Assumptions/dependencies: Funding and compute; community benchmarks; shared scaffolds (OpenHands, verl).
  • Sustainability/Cost Optimization: RM-driven selective CI to reduce energy and spend
    • Use case: Use calibrated scores to decide which tests to run, deferring expensive suites unless confidence is low.
    • Sector: DevOps, sustainability
    • Tools/products/workflows: Cost-aware CI orchestrators; adaptive test budgets; RM-informed scheduling.
    • Assumptions/dependencies: Robust calibration to avoid missing regressions; governance to balance risk and savings.
  • Consumer/Developer Assistants: Personal agents with multi-candidate ranking and confidence coaching
    • Use case: Assist non-experts by proposing several fixes and explaining confidence-driven trade-offs; guide learning via near-miss identification.
    • Sector: Daily life, education
    • Tools/products/workflows: Lightweight clients calling RM services; pedagogical UIs showing reliability diagrams; explanation modules.
    • Assumptions/dependencies: Affordable inference; careful UX to prevent overreliance; transparency about uncertainty and limitations.
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