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
A selective harness-rescue training method uses specialized sibling harnesses to replace failed reinforcement learning rollouts and transfer their benefits into a single policy that performs better under the original base harness.
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…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Harness-S: Learning from Sibling Harnesses for Generalizable Agent Training was first seen on Hugging Face · Daily papers on Nov 6, 2026. Since then it has appeared in 31 checks on 3 different days. Its best position was #3 of 30, reached on Nov 6, 21:28 UTC. It is #5 right now, holding steady at #5 over the last 6 checks.
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Yes. As of Nov 8, 19:28 UTC it is #5, holding steady at #5 over the last 6 checks.
#3 on Hugging Face · Daily papers, on Nov 6, 21:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 6, 2026 and has been on the list on 3 days (31 checks) since.