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arxiv: 2606.27136 · v1 · pith:NOR6MQLJnew · submitted 2026-06-25 · 💻 cs.AI

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

Shicheng Ye , Chao Yu This is my paper

Pith reviewed 2026-06-26 04:30 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentsexperiential rulespolicy optimizationinteractive environmentsAlfWorldWebShopjoint learningtrajectory-based revision
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The pith

LLM agents improve by jointly updating experiential rules and their policy from the same trajectories.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that LLM agents in multi-step environments benefit when experiential rules and the policy are revised together rather than separately. Existing approaches either store rules outside the model for prompting or update model parameters from trajectories, but the first can drift out of sync with the policy while the second gives only limited local correction. JERP retrieves relevant rules at decision time to condition the agent and then uses each episode's trajectories both to optimize the policy and to revise the rule pool by comparing rollouts against reference successful ones. This coupling produces consistent performance gains on AlfWorld and WebShop. A sympathetic reader would care because it offers a way to accumulate stable, interpretable behaviors while still allowing the underlying model to improve.

Core claim

JERP updates a long-term experiential-rule pool and the policy from the same interaction trajectories. At decision time it 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.

What carries the argument

The shared-trajectory revision step that compares current rollouts to reference successful trajectories to update both the rule pool and the policy parameters.

If this is right

  • Rules stay aligned with the current policy as it changes through interaction.
  • Stable successful behaviors can be absorbed into the model parameters over time.
  • The approach yields consistent gains on complex interactive tasks such as AlfWorld and WebShop.
  • Sparse-reward settings receive both broad policy improvement and targeted local correction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could reduce reliance on manually written or separately maintained rule sets.
  • Similar joint-update logic might be tested in other agent architectures that separate memory from parameters.
  • Over longer horizons the rule pool might serve as an interpretable audit trail of what the policy has internalized.

Load-bearing premise

That comparing current rollouts with reference successful trajectories produces useful revisions to the rule pool that remain aligned with the evolving policy without introducing errors or misalignment.

What would settle it

An experiment in which rule revisions produced by the comparison step cause the agent's decisions to degrade relative to a policy-only update baseline on the same tasks.

Figures

Figures reproduced from arXiv: 2606.27136 by Chao Yu, Shicheng Ye.

Figure 1
Figure 1. Figure 1: Comparison between JERP and two basic paradigms. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: B. Trajectory Sampling Because the rule pool K (d) for task d expands during training, we do not feed all rules into the model in each episode. Instead, we select a size-controlled subset of rules for the current decision process. Since the working rules are already organized by task, the current implementation does not perform further instance-level relevance retrieval. The system sorts rules in K (d) by … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the JERP framework. the current rule pool, current trajectory group, and reference successful trajectories, and then obtains the updated long￾term experiential-rule pool. This idea is close to ExpeL [9], which maintains natural-language rules through experiential reflection; both approaches prompt an LLM to generate rule￾update operations. We further introduce a merge operation to combine seman… view at source ↗
Figure 3
Figure 3. Figure 3: Success-rate curves of JERP and its ablated variant on AlfWorld over [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Success-rate curves of JERP and baseline methods on different AlfWorld task types over training steps. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes JERP, a method for LLM agents in multi-step interactive environments that maintains a long-term experiential-rule pool and jointly updates both the rule pool and the policy parameters from the same interaction trajectories. At inference, task-relevant rules are retrieved and used to condition the agent alongside interaction history; after each episode, trajectories are used both for policy optimization and for revising the rule pool via comparison of current rollouts against reference successful trajectories. The central claim is that this coupling keeps rules aligned with the evolving policy and produces consistent decision-performance gains on complex tasks, as demonstrated on AlfWorld and WebShop.

Significance. If the empirical results and alignment mechanism hold under scrutiny, the work would offer a concrete way to combine the interpretability and stability of external natural-language rules with the broader adaptability of policy updates, addressing a recognized tension in LLM-agent literature between rule prompting and parameter tuning.

major comments (2)
  1. [Abstract / §3] Abstract / §3 (Method): the rule-revision step is described only as 'comparing current rollouts with reference successful trajectories' with no specification of reference selection criteria, the comparison algorithm or prompt template, or any safeguard against propagating errors when references are suboptimal or when partial information in the current rollout is useful. This mechanism is load-bearing for the claimed policy-rule alignment benefit.
  2. [§4] §4 (Experiments): the abstract asserts 'consistent gains' on AlfWorld and WebShop, yet the provided text contains no quantitative results, baseline comparisons, number of runs, variance estimates, or error analysis, preventing assessment of whether the joint-learning advantage is supported by the data.
minor comments (1)
  1. [Abstract] Abstract: the description of the retrieval step ('retrieves task-relevant rules') would benefit from a short statement of the retrieval method or similarity metric used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback. The comments highlight areas where the current manuscript description is insufficiently precise, and we will revise accordingly to strengthen the presentation of the method and results.

read point-by-point responses

  1. Referee: [Abstract / §3] Abstract / §3 (Method): the rule-revision step is described only as 'comparing current rollouts with reference successful trajectories' with no specification of reference selection criteria, the comparison algorithm or prompt template, or any safeguard against propagating errors when references are suboptimal or when partial information in the current rollout is useful. This mechanism is load-bearing for the claimed policy-rule alignment benefit.

    Authors: We agree that the current description in the abstract and §3 is high-level and does not provide the requested implementation details. In the revised manuscript we will expand §3 with: (i) reference selection criteria (trajectories drawn from a replay buffer of prior successful episodes, ranked by cumulative reward); (ii) the comparison procedure (an LLM prompt that performs structured difference analysis between the current rollout and the reference, then proposes candidate rule edits); (iii) the exact prompt template used; and (iv) safeguards (a reward-margin filter that only accepts a rule update when the new trajectory strictly outperforms the reference, plus an optional human-in-the-loop verification step for the initial rule pool). These additions will make the alignment mechanism explicit and address concerns about error propagation. revision: yes

  2. Referee: [§4] §4 (Experiments): the abstract asserts 'consistent gains' on AlfWorld and WebShop, yet the provided text contains no quantitative results, baseline comparisons, number of runs, variance estimates, or error analysis, preventing assessment of whether the joint-learning advantage is supported by the data.

    Authors: The referee is correct that the version under review lacks the quantitative experimental section. We will add a complete §4 containing: success-rate tables for AlfWorld and WebShop with means and standard deviations over multiple random seeds, direct comparisons against the listed baselines, and a short error analysis of failure modes. This will allow readers to evaluate the claimed gains. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical joint-update procedure is self-contained

full rationale

The paper presents JERP as an algorithmic method that collects trajectories, optimizes the policy, and revises the rule pool via direct comparison to reference trajectories. No equations, derivations, or parameter-fitting steps are described that reduce to their own inputs. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The alignment claim is an empirical hypothesis about the procedure's effect, not a definitional or fitted tautology. This matches the default case of a non-circular empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No specific free parameters, axioms, or invented entities are identifiable from the abstract alone; the approach appears to build on standard LLM prompting, retrieval, and trajectory-based optimization without new postulated entities.

pith-pipeline@v0.9.1-grok · 5728 in / 1124 out tokens · 50327 ms · 2026-06-26T04:30:45.754742+00:00 · methodology

discussion (0)

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