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Source note3 min read

Joint Learning of Experiential Rules and Policies for Large Language Model Agents

Source

arXiv

arxiv.org/abs/2606.27136v1

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Plain-Language Brief

Researchers present a method called Joint Learning of Experiential Rules and Policies for Large Language Model Agents (JERP). This method aims to help LLM agents, which operate in multi-step interactive environments, make better use of their interaction experiences. Instead of keeping rules outside the model or using trajectories and feedback to update model parameters separately, JERP updates a long-term experiential-rule pool and the policy from the same interaction trajectories. The method is designed to keep the rule pool aligned with the evolving policy while allowing stable behaviors to be gradually absorbed into the model itself. Experiments on AlfWorld and WebShop demonstrate consistent gains in decision performance for complex interactive tasks.

What the Document Says

  • The research introduces Joint Learning of Experiential Rules and Policies for Large Language Model Agents (JERP).
  • This method uses interaction trajectories to update a long-term experiential-rule pool and the policy.
  • At decision time, it retrieves task-relevant rules and conditions the agent on them together with the interaction history.
  • After each episode, it optimizes the policy and revises the rule pool by comparing current rollouts with reference successful trajectories.
  • The coupling of updating both policy and rule pool keeps the rule pool aligned with the evolving policy.
  • The method allows stable behaviors to be gradually absorbed into the model itself.

Key Concepts Mentioned

  • Large Language Model Agents (LLM)
  • Experiential Rules
  • Policies
  • Interactive Environments
  • Decision Performance

Claims Made by the Document

  • JERP yields consistent gains in decision performance for complex interactive tasks.
  • The coupling of updating both policy and rule pool keeps the rule pool aligned with the evolving policy.

Evidence Used

  • Experiments on AlfWorld and WebShop

Methods / Approach

  • Updating a long-term experiential-rule pool and the policy from interaction trajectories.
  • Retrieving task-relevant rules at decision time.
  • Comparing current rollouts with reference successful trajectories.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The research does not discuss potential limitations of the proposed method due to space constraints or focus on the method's benefits.

Open Questions

  • What are the specific algorithms used for rule retrieval and optimization in JERP?
  • How does JERP handle situations where there is a lack of successful reference trajectories?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide explicit details about the algorithms used for rule retrieval and optimization, nor does it discuss how the method handles cases with insufficient successful reference trajectories.

Raw Abstract / Excerpt

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.