arXiv cs.AI· Zhen Bi, Zhizhi Peng, Yan Wang, Xueshu Chen, Haofei Zhang, Yaobo Zhang, Zeyu Yang, Qing Shen, Jungang Lou·· 2 小时前AI 评分41
HaMo 提出 Harness-Model 协同框架，实现长程持续学习
Harness-Model Collaboration for Long-Horizon Continual Learning
AI 导读
arXiv 论文提出 HaMo，一种 Harness-Model 协同框架，结合快速非参数经验积累与慢速参数化适应，用于长程智能体持续学习。HaMo 将轨迹蒸馏为策略级经验卡以便即时检索，并用基于经验条件的持续学习目标训练模型在新情境中执行检索到的策略，同时以检索覆盖率为基础选择互补轨迹做参数更新。
正文 · 原文
Abstract:Long-horizon agents continuously encounter reusable procedural knowledge, yet existing systems typically rely either on model adaptation or external memory, trading persistent learning for rapid reuse. This limitation is especially important for Web agents that repeatedly execute related multi-step workflows. We propose HaMo, a Harness-Model collaborative framework that combines fast non-parametric experience accumulation with slow parametric adaptation. HaMo distills trajectories into strategy-level experience cards for immediate retrieval, while an experience-conditioned continual learning objective trains the model to interpret, ground, and execute retrieved strategies across changing contexts. A retrieval-coverage-based selection mechanism further identifies compact and complementary trajectories for parameter updates. Experiments show that HaMo outperforms harness-only and model-only adaptation under within-domain and cross-domain shifts, improving both retention and adaptation. These results highlight the importance of jointly preserving procedural experience and internalizing the competence to transfer and execute it across tasks.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.34724 [cs.AI] |
| (or arXiv:2610.34724v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.34724 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhen Bi [view email]
[v1]
Mon, 26 Oct 2026 02:21:20 UTC (1,880 KB)
来源：arXiv cs.AI · arxiv.org