Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents
Reinforcement learning with verifiable rewards (RLVR) provides reliable supervision for software engineering agents through executable tests, but binary rewards fail to distinguish trajectories with substantially different debugging quality.
This research introduces Behavioral Self-Rewarding (BSR), a method to enhance reinforcement learning with verifiable rewards (RLVR) for software engineering agents by enabling policies to self-assess debugging quality without external reward models.
What happened
The article presents BSR, a technique that allows software engineering agents to evaluate their own debugging trajectories by comparing observable behaviors. This approach generates auxiliary rewards through pairwise preference aggregation, improving task performance without additional environment interactions.
Why it matters
This advancement addresses a critical limitation in RLVR systems, enabling more nuanced learning for software engineering tasks. It reduces dependency on external reward models while improving debugging quality assessments.
- BSR provides a novel way to refine rewards using policy-generated comparisons
- The approach maintains compatibility with existing verification systems
- It reduces the need for external reward modeling infrastructure
- Experimental results validate effectiveness across multiple model sizes
- Self-rewarding mechanisms can enhance debugging quality assessments
The bigger picture
The method demonstrates consistent performance improvements in software engineering tasks, offering a scalable approach to enhance agent training through self-supervised reward refinement.
The research demonstrates that software engineering agents can effectively self-assess debugging quality through behavioral comparisons, offering a promising approach to improve RLVR systems without external reward models.
Technical details
- Reinforcement Learning with Verifiable Rewards (RLVR)
- Behavioral Self-Rewarding (BSR) framework
- Pairwise preference aggregation
- Task-local score generation
- Auxiliary reward formulation
FAQ
What problem does BSR solve?
BSR addresses the limitation of binary rewards in RLVR systems, which cannot distinguish between trajectories with varying debugging quality.
How does BSR differ from traditional reward models?
BSR uses the policy itself to generate rewards through trajectory comparisons rather than relying on separately trained reward models.
What are the key technical contributions?
The method introduces task-local score aggregation from pairwise preferences and demonstrates how to create bounded auxiliary rewards while preserving verification outcomes.
What models were tested?
Experiments were conducted on Qwen3-14B and Qwen3-32B configurations using SWE-bench Verified and SWE-bench-Live datasets.
What is the main benefit of this approach?
It enables more nuanced learning for software engineering agents by providing additional supervision from existing trajectories without new environment interactions.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.AI on Oct 12, 2026.
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