Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training

Kangning Zhang, Wenxiang Jiao, Bowen Fang, Shuai Shao, Jiachen Zhu, Rong Shan, Shijian Wang, Hao Wang, Yuan Lu, Weiwen Liu, Weinan Zhang

Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training: 70 upvotes on Hugging Face Daily Papers, #4 of 32 papers on 2026-11-06. Day-by-day upvote history.

Upvotes over time

It had 54 upvotes by its first day, 63 upvotes by the next day, 67 upvotes by day 3, 70 upvotes by day 7, and has 70 now. It ranked #12 of 207 papers in its week and #15 of 340 in its month. Its code repository has 69 GitHub stars.

Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair different failures, and their utility changes as the policy learns. Rather than seeking a universally superior harness, we introduce Harness-S to selectively rescue failures of a base execution system through sibling harnesses and transfer the resulting capabilities into a single policy that remains effective under the original harness. Directional Harness Evolution develops specialized sibling harnesses along distinct repair directions, forming a reusable Sibling Harness Pool that remains fixed during policy training. Complementary Harness Rescue Training invokes this pool only when all initial attempts under the base harness fail. The same policy generates fresh rollouts under selected auxiliary harnesses, and these trajectories replace the failed group for reinforcement learning. An Adaptive Harness Router selects the Top-k variants using Harness Behavior Profiles and voting across multiple LLMs. Policy-aware profile re-anchoring refreshes this evidence as the policy learns. Across AppWorld, SWE-Gym, and DBBench, the trained policy substantially outperforms policy-only training when returned to the original base harness. Behavioral analyses and held-out cases support the transfer of harness-elicited behaviors into the policy itself. Reusing the router for Rank-1 inference provides additional gains at the same executor rollout count.

Paper page on Hugging Face · arXiv

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