Robust Code RL via Faulty-Code-Driven Test case Synthesis and Dense Reward Shaping

The paper introduces RobustTests, a framework that improves reinforcement learning for code generation by synthesizing test cases from faulty code and refining rewards with a dense, stepwise function. It uses validator agents and behavioral clustering to filter out invalid or redundant tests, and incorporates pass‑rate‑based rewards to counter hallucination noise. Experiments on CodeContests and LiveCodeBench show that fine‑tuning Qwen3‑32B with RobustTests yields a 3% absolute performance gain over baseline methods.

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• llms
• agents
• reinforcement-learning
• fine-tuning
• benchmarks
• safety