Training Language Agents to Learn from Experience
Researchers have introduced a new framework called In-context Training (ICT) for evaluating how language agents can learn from experience. This framework allows agents to improve their performance on future tasks by generating prompts based on past interactions. The study demonstrates that language agents can learn to self-improve, even in environments different from those they were trained in.
- ▪The In-context Training (ICT) task evaluates cross-task self-improvement in language agents.
- ▪A reflector model observes trajectories from an actor model to enhance future performance.
- ▪The study shows that trained reflectors outperform untrained baselines in various task families.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20477 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Machine Learning arXiv:2605.20477 (cs) [Submitted on 19 May 2026] Title:Training Language Agents to Learn from Experience Authors:Yuval Shalev, Zifeng Ding, Mateja Jamnik View a PDF of the paper titled Training Language Agents to Learn from Experience, by Yuval Shalev and 1 other authors View PDF HTML (experimental) Abstract:Language agents can adapt from experience in interactive environments, but current reflection-based methods can only self-correct within a single task instance. Whether such experience can be distilled into reusable lessons that improve performance on future unseen tasks remains unclear. We address this problem by introducing the In-context Training (ICT) task, a framework for evaluating cross-task self-improvement in language agents.
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