Improving Reinforcement Learning for Coding Agents
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Key Takeaways
- Integrates existing coding-agent harnesses with scalable policy-gradient training without breaking native control flow.
- Achieves a rollout to training probability correlation above 0.99 for consistent model alignment.
- Implements sandbox orchestration with image caching and staged defenses to block reward hacking.
- Uses termination aware trajectory filtering to handle asynchronous data processing in agent workflows.
Summary & Methodology Analysis
Lego-RL addresses the structural friction between reinforcement learning, which uses policy-gradient optimization to improve model behavior based on feedback, and coding harnesses designed for software execution. By using in-process LLM proxying to intercept raw tokens and metadata at the API boundary, the framework bridges the gap between training and inference. It utilizes a sparse mixture of experts model, a technique where only a subset of the model parameters are activated for any given input, specifically the Qwen3.5-35B-A3B architecture, to maintain performance while replaying expert routing decisions during the rollout phase.
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary problem this framework solves?
It solves the alignment issues between coding harnesses and reinforcement learning, which often result in environment crashes and reward hacking.
Q2. Does this tool work with multiple model architectures?
The paper states that evaluation is limited to the Qwen3.5-35B-A3B architecture, and generalization to other models remains to be evaluated.
Q3. How does it protect against reward manipulation?
It uses staged sandbox defenses to mitigate observed reward-exploiting strategies.
Q4. What performance metric indicates successful training alignment?
The framework maintains a rollout to training probability correlation above 0.99.
Q5. What dataset is used for evaluation?
The paper uses the SWE-bench Verified dataset.
Q6. Does the system provide automated causal verification for failures?
No. The diagnostic tools identify plausible causes from evidence but do not provide automated causal verification.
Q7. Does the framework guarantee total protection against all forms of reward hacking?
No. The defenses address the failure modes observed in the experiments but do not guarantee robustness against all possible reward-exploiting strategies.
Q8. Can this be used for mixed-harness training?
The paper states that mixed-harness training remains to be evaluated.
Q9. What is the role of the Live UI?
The Live UI is part of an integrated plugin that provides run validation, monitoring, and diagnostics for training trajectories.