这篇论文提出了JERP，让LLM智能体边干活边总结经验规则，在AlfWorld和WebShop上效果明显。
JERP（Joint Learning of Experiential Rules and Policies）是一种让LLM智能体同时从交互轨迹中更新经验规则池和策略的方法。该方法在决策时检索任务相关规则，并结合交互历史指导行动；每轮结束后，利用轨迹对比参考成功案例来优化策略与修正规则池。在AlfWorld和WebShop两个基准测试上，JERP在复杂交互任务中持续提升了决策性能。这一耦合机制使规则池与演化策略保持同步，同时将稳定有效行为逐步吸收到模型参数中。
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
For LLM agents in multi-step interactive environments, a key challenge is to make effective use of accumulated interaction experience. Existing work has typically separated two uses of such experience: keeping it outside the model as natural-language rules for later prompting, or using trajectories and feedback to update the model parameters. The former is easy to interpret but can fall out of sync with the evolving policy; the latter improves the policy more broadly but provides only limited correction for local mistakes in sparse-reward settings. We present Joint Learning of Experiential Rules and Policies for LLM Agents (JERP), which updates a long-term experiential-rule pool and the policy from the same interaction trajectories. At decision time, JERP retrieves task-relevant rules and conditions the agent on them together with the interaction history. After each episode, it uses the collected trajectories both to optimize the policy and to revise the rule pool by comparing current rollouts with reference successful trajectories. This coupling keeps the rule pool aligned with the evolving policy while allowing stable and effective behaviors to be gradually absorbed into the model itself. Experiments on AlfWorld and WebShop show that JERP yields consistent gains in decision performance for complex interactive tasks.