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arXiv AI By Yiran Wu, Andrew Zhao, Yurui Chang, Qingyun Wu, Xuan Feng

Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents

Read the original on arXiv AI →

The paper introduces Behavioral Self-Rewarding (BSR), a method that lets reinforcement learning agents with verifiable rewards judge and compare their own debugging trajectories to generate additional bounded auxiliary rewards. BSR aggregates pairwise preferences among trajectories that share the same verifier outcome, adding reward contrast while maintaining the original pass/fail ordering. Experiments on SWE-bench Verified and SWE-bench-Live with Qwen3-14B and Qwen3-32B show consistent performance gains, demonstrating that a policy’s own comparative judgments can serve as a useful learning signal.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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