// Behavioral Self-Rewarding for SWE agents //

It's standard practice to train SWE agents on binary test rewards.

This paper shows the problem with that. When every trajectory in a group passes, or every one fails, the reward has no contrast, and the group teaches nothing.

Researchers from Penn State and Microsoft Research propose Behavioral Self-Rewarding. The policy compares its own trajectories in pairs, and those preferences become small bounded rewards added on top of the test result.

The added rewards are bounded, so a self-judged preference can rank two passing trajectories but can never lift a failing one above a passing one.

No new environment interactions are needed. On SWE-bench Verified, Qwen3-14B improves from 32.1% to 34.7% average accuracy at a 400-step budget, and on SWE-bench-Live from 6.33% to 8.11%.

https://arxiv.org/abs/2610.12598

Chat with Paper: https://academy.dair.ai/papers/beyond-binary-verifiable-rewards-behavioral-self-rewarding-for-swe-agents-2610.12598