Reinforcement learning with verifiable rewards (RLVR) trains software engineering agents by rewarding patches that pass executable tests.
Self-judged trajectories improve SWE agent training beyond binary verifier rewards
Behavioral Self-Rewarding aggregates pairwise preferences from the current policy into bounded auxiliary rewards that preserve pass/fail ordering.
Big Tech
Yiran Wu · Andrew Zhao · Yurui Chang · Qingyun Wu · Xuan Feng
Pennsylvania State University · AG2AI · Microsoft Research
Research Digest··3 min read
The authors introduce Behavioral Self-Rewarding (BSR), which reuses the current policy to compare trajectories sharing the same test outcome, producing auxiliary rewards for reinforcement learning with verifiable rewards.
Why this paper
From Microsoft Research and 2 others
In one line
Behavioral Self-Rewarding uses the policy's own trajectory comparisons to improve SWE agent training beyond binary test rewards.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§