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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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Source

Plain-Language Brief

The paper introduces a new method called Joint Learning of Experiential Rules and Policies (JERP) for large language model agents operating in multi-step interactive environments. The main challenge addressed is effectively utilizing accumulated interaction experience, which JERP tackles by updating a long-term experiential-rule pool and the policy from the same interaction trajectories.

What the Document Says

  • The paper presents a method called Joint Learning of Experiential Rules and Policies (JERP) for large language model agents.
  • JERP updates a long-term experiential-rule pool and the policy from interaction trajectories.
  • At decision time, JERP retrieves task-relevant rules and conditions the agent on them along with the interaction history.
  • After each episode, it optimizes the policy and revises the rule pool by comparing current rollouts with reference successful trajectories.
  • JERP 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 consistent gains in decision performance for complex interactive tasks.

Key Concepts Mentioned

  • large language model agents
  • multi-step interactive environments
  • experiential rules
  • policies
  • decision performance

Claims Made by the Document

  • Existing work separates two uses of accumulated interaction experience.
  • The former (natural-language rules) can fall out of sync with the evolving policy.
  • The latter (trajectories and feedback) improves the policy but provides limited correction for local mistakes in sparse-reward settings.
  • JERP addresses these challenges by coupling rule pool updates and policy optimization from interaction trajectories.
  • JERP yields consistent gains in decision performance for complex interactive tasks.

Evidence Used

  • The text describes the JERP methodology, its components, and its reported experimental results.

Methods / Approach

  • Joint Learning of Experiential Rules and Policies (JERP)

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The paper does not discuss potential limitations or challenges in implementing JERP in real-world applications.
  • It is unknown whether the reported gains hold true for a wide range of complex interactive tasks.

Open Questions

  • What are the specific algorithmic details of JERP?
  • How does JERP handle situations where no successful trajectory is available for reference?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper primarily focuses on describing the method and reporting experimental results, so it may be beneficial to consult additional resources or the authors themselves for further details.
  • Questions about specific algorithmic details, limitations, or practical implementation should be directed towards experts in the field or the authors of the paper.

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.