[Submitted on 10 Oct 2026]
Title:Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation
View a PDF of the paper titled Evolving to Match Capabilities: Improving Harness Self-Evolution via Skill Adaptation, by Zhihao Yang and 9 other authors
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Abstract:Harness self-evolution offers a practical route for LLM agents to improve from execution experience without updating model parameters. Its effectiveness depends on whether models can successfully act on the guidance provided by their harnesses. However, we find that harness benefits are not uniform across model capabilities: skills that improve stronger models can provide little benefit or even degrade performance for weaker ones. Across seven models and three benchmarks, we identify 103 task-skill pairs where skills improve GPT-5.6-luna by 31.1 points but degrade Gemma-4-E4B by 8.8 points. In 86% of failed weak-model runs, the violated requirement is already stated in the skill. This motivates skill adaptation: reformulating existing skills to match the executor's capabilities and make their guidance easier to execute. To achieve this, we diagnose failures in tool execution, context management, instruction following, and verification, and distill these diagnoses into a fixed meta-skill that guides adaptation during harness self-evolution. Guided by the meta-skill, a capable external rewriter, Kimi-K3, adapts skills without execution evidence, improving the three weak executors by 5.1-14.1 points over the original skills. We then incorporate this adaptation into a five-round self-evolution loop, where each model revises its own skill from execution trajectories and verifier feedback. Across seven executors, from Gemma-4-E4B to Kimi-K2.5, the meta-skill consistently improves final scores by 2.0-5.1 points over the same loop without the meta-skill. Our findings provide practical design guidance for developing more effective harness self-evolution methods.
Comments: 24 pages, 12 figures, 4 tables. Project page: this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2610.33024 [cs.LG]
(or arXiv:2610.33024v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.33024
arXiv-issued DOI via DataCite
Submission history
From: Zhihao Yang [view email] [v1] Sat, 10 Oct 2026 04:44:35 UTC (1,621 KB)
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