🤖 AI Summary
This work addresses the challenge that large language model agents struggle to effectively leverage accumulated experience during multi-step interactions, as existing approaches decouple rule-based guidance from policy learning. The paper proposes JERP, a novel framework that tightly couples experiential rule learning with policy optimization for the first time. Within JERP, both the policy model and a persistent experience rule pool are updated synchronously from the same interaction trajectory. During decision-making, the agent retrieves relevant rules and conditionally generates actions informed by historical context; post-interaction, it jointly refines the policy and revises rules using the complete trajectory. This dynamic alignment between rules and policy enables stable internalization of behavior. Empirical results demonstrate significant improvements in decision-making performance on complex tasks in the AlfWorld and WebShop benchmarks.
📝 Abstract
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.