EvoHarnessRL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents
However, effective harness use raises two coupled challenges: state formation from noisy interaction traces and runtime control over external-state access. Existing agents usually handle both through prompts, heuristics, or domain-specific conventions, leaving the external workspace and its usage policy manually engineered. To address this, we study the problem of harness policy learning, where agents learn harness policies offline and deploy them to construct and update external harness state online during runtime task execution.
- ▪However, effective harness use raises two coupled challenges: state formation from noisy interaction traces and runtime control over external-state access.
- ▪Existing agents usually handle both through prompts, heuristics, or domain-specific conventions, leaving the external workspace and its usage policy manually engineered.
- ▪To address this, we study the problem of harness policy learning, where agents learn harness policies offline and deploy them to construct and update external harness state online during runtime task execution.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2608.05446 |
| Publication time | Mon, 10 Aug 2026 21:28:07 +0000 |
| Retrieval time | 2026-08-10T21:30:45.332Z |
| Last seen | 2026-08-10T21:30:45.332Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Machine Learning arXiv:2608.05446 (cs) [Submitted on 5 Aug 2026] Title:EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents Authors:Xuying Ning, Dongqi Fu, Tianxin Wei, Hanqing Zeng, Yuanchen Bei, Bingxuan Li, Zihao Li, Qifan Wang, Xiang Shen, Yifan Wu, Jiayi Liu, Hong Li, Yinglong Xia, Xiangjun Fan, Hanghang Tong, Jingrui He View a PDF of the paper titled EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents, by Xuying Ning and 15 other authors View PDF HTML (experimental) Abstract:Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse experience across interactions.
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