让大模型从单次经验中提炼通用策略，提升任务适应力。
Experiential Reflective Learning for Self-Improving LLM Agents
- 通过反思任务过程生成可复用的行动法则
- 在Gaia2上成功率提升7.8%，可靠性显著增强
- 适合需要持续学习与跨任务迁移的智能体系统
大语言模型驱动的自主智能体虽具备复杂推理能力，但在专用环境中的适应性差，无法利用过往经验。本文提出体验式反思学习（ERL），通过反思任务轨迹与结果，生成可迁移的行动启发式规则。测试时，根据当前任务检索相关启发式并注入上下文以指导执行。在Gaia2基准上，相比ReAct基线，成功率达7.8%提升，且优于已有经验学习方法。消融实验表明，选择性检索至关重要，启发式规则比少样本轨迹提示更具迁移性。结果证明，从单次经验中提取可迁移启发式，是实现有效智能体自进化的关键。
原文摘要 · Abstract (English)
Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes to generate heuristics, capturing actionable lessons that transfer across tasks. At test time, relevant heuristics are retrieved based on the current task and injected into the agent's context to guide execution. On the Gaia2 benchmark, ERL improves success rate by 7.8% over a ReAct baseline, with large gains in task completion reliability, and outperforms prior experiential learning methods. Through systematic ablations, we find that selective retrieval is essential and that heuristics provide more transferable abstractions than few-shot trajectory prompting. These results demonstrate that reflecting on single-attempt experiences to extract transferable heuristics enables effective agent self-improvement.
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