Great work from Microsoft and colleagues.

This one is on the hot new topic of co-evolving the harness and the model.

(bookmark it)

They raise Qwen3.5-35B-A3B's average resolve rate on three coding benchmarks from 57.8% to 64.8% by alternating the two.

In each round, the policy reads its own failures and proposes harness changes. Then it is trained with RL under the harness it picked. Tasks that stay hard during training become the search set for the next harness round.

The joint gain is larger than the harness-only gain and the policy-only gain added together. Each optimized part also keeps its improvement when paired with the other's original version.

Using Opus 4.8 as the harness proposer in the same loop adds more on top, which makes this a practical way to transfer a frontier model's harness judgment into a smaller policy.

https://arxiv.org/abs/2610.22791

Chat with Paper: https://academy.dair.ai/papers/self-evolving-coding-agents-via-harness-policy-optimization-2610.22791