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arxiv: 2605.31509 · v1 · pith:YZDYMQXZnew · submitted 2026-05-29 · 💻 cs.LG · cs.AI

Skill Reuse as Compression in Agentic RL

Zhikun Xu , Yu Feng , Jacob Dineen , Taiwei Shi , Jieyu Zhao , Ben Zhou This is my paper

Pith reviewed 2026-06-28 22:46 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords skill reuseminimum description lengthagentic RLPAC-Bayes boundgeneralizationtrajectory segmentationLLM agentscompression
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The pith

Agents generalize better when their successful trajectories are decomposed into a small set of reusable abstract patterns.

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

The paper claims that large language model agents often learn brittle shortcuts because reinforcement learning does not explicitly reward structural compressibility. ReuseRL addresses this by grounding training in the minimum description length principle: it builds a shared skill dictionary from successful trajectories and adds a segmentation cost to the objective that penalizes unique, hard-to-reuse behaviors. A PAC-Bayes bound is derived to show this penalty improves generalization. Experiments demonstrate higher success rates both within and outside the training distribution on ALFWorld, TextWorld-Cooking, and Countdown-Stepwise compared to standard GRPO and length-based methods.

Core claim

By extracting a shared skill dictionary from successful trajectories and augmenting the RL objective with a segmentation cost that penalizes idiosyncratic behaviors, ReuseRL formalizes skill reuse as compression under the minimum description length principle and proves a corresponding PAC-Bayes generalization bound.

What carries the argument

The segmentation cost based on the shared skill dictionary extracted from successful trajectories, which is added to the RL objective to enforce compressibility.

If this is right

  • Improved in-distribution and out-of-distribution success rates in agentic tasks.
  • Explicit penalization of non-reusable trajectory patterns leads to more generalizable policies.
  • Applicability across diverse environments including ALFWorld, TextWorld-Cooking, and Countdown-Stepwise.
  • Grounding in MDL provides a theoretical justification via the PAC-Bayes bound.

Where Pith is reading between the lines

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

  • If the compression approach holds, it could extend to encouraging reusable skills in non-agent reinforcement learning settings.
  • The skill dictionary might be learnable online during training rather than extracted only from successful trajectories.
  • Similar compression penalties could apply to other sequence modeling tasks outside RL.

Load-bearing premise

That adding the segmentation cost from the skill dictionary to the RL objective will produce the claimed generalization improvement as justified by the PAC-Bayes bound.

What would settle it

Observing no improvement in out-of-distribution success when the segmentation cost is included, or when the extracted dictionary fails to reduce the description length of trajectories.

Figures

Figures reproduced from arXiv: 2605.31509 by Ben Zhou, Jacob Dineen, Jieyu Zhao, Taiwei Shi, Yu Feng, Zhikun Xu.

Figure 1
Figure 1. Figure 1: REUSERL distinguishes reusable compression from raw brevity. Each colored box is one ALFWorld atomic skill. Vanilla GRPO optimizes task success alone and can produce long trajectories with repeated or wasted steps. A pure round-length penalty is a degenerate singleton-only code that penalizes all steps uniformly, including the necessary search, so the agent under-explores and never reaches the target. REUS… view at source ↗
read the original abstract

Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generalize better when their successful trajectories are structurally compressible, decomposed into a small set of reusable abstract patterns. To formalize this, we introduce ReuseRL, which grounds agentic RL in the Minimum Description Length (MDL) principle. ReuseRL extracts a shared skill dictionary from successful trajectories and augments the RL objective with a segmentation cost, explicitly penalizing idiosyncratic behaviors that encode poorly. We prove a PAC-Bayes generalization bound for this compression penalty. Across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise, ReuseRL improves in- and out-of-distribution success over vanilla GRPO and strong round-length baselines.

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

3 major / 1 minor

Summary. The paper introduces ReuseRL for agentic RL, grounding training in the Minimum Description Length principle. It extracts a shared skill dictionary from successful trajectories, augments the RL objective (specifically GRPO) with a segmentation cost that penalizes non-compressible behaviors, proves a PAC-Bayes generalization bound for the compression penalty, and reports improved in- and out-of-distribution success rates on ALFWorld, TextWorld-Cooking, and Countdown-Stepwise relative to vanilla GRPO and round-length baselines.

Significance. If the PAC-Bayes derivation is valid and the empirical gains are reproducible with proper statistical controls, the work offers a principled compression-based approach to mitigating brittle shortcuts in LLM agents, linking MDL directly to RL objectives in a way that could generalize across domains.

major comments (3)
  1. [Abstract] Abstract: the claim that a PAC-Bayes bound is proved cannot be assessed because no derivation steps, assumptions, or explicit form of the bound are supplied; this is load-bearing for the central theoretical contribution.
  2. [Abstract] Abstract / Method (implied): the segmentation cost derived from the shared skill dictionary and its precise addition to the GRPO objective are not specified, so it is impossible to verify whether the claimed generalization improvement follows from the bound or reduces to a fitted hyperparameter.
  3. [Abstract] Abstract: no dataset details, number of runs, or statistical reporting (e.g., means, variances, or significance tests) are provided for the reported success-rate improvements, undermining the empirical claim.
minor comments (1)
  1. [Abstract] Abstract: the definition of out-of-distribution splits and the exact baseline implementations (round-length) should be clarified for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed comments on the abstract. We address each point below and propose revisions to improve clarity while preserving the manuscript's core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that a PAC-Bayes bound is proved cannot be assessed because no derivation steps, assumptions, or explicit form of the bound are supplied; this is load-bearing for the central theoretical contribution.

    Authors: The full derivation, including all assumptions (e.g., bounded loss and prior over policies) and the explicit PAC-Bayes bound form, appears in Section 4. The abstract is intentionally concise, but we agree it should better signal the result. We will revise the abstract to include a one-sentence high-level statement of the bound and its key assumptions. revision: yes

  2. Referee: [Abstract] Abstract / Method (implied): the segmentation cost derived from the shared skill dictionary and its precise addition to the GRPO objective are not specified, so it is impossible to verify whether the claimed generalization improvement follows from the bound or reduces to a fitted hyperparameter.

    Authors: Section 3.1 defines the segmentation cost as the MDL length of the trajectory under the extracted skill dictionary; Section 3.2 shows its exact additive term in the GRPO objective (Equation 5). The bound in Section 4 is derived directly from this term. We will add a brief clause to the abstract describing the cost and its placement in the objective. revision: yes

  3. Referee: [Abstract] Abstract: no dataset details, number of runs, or statistical reporting (e.g., means, variances, or significance tests) are provided for the reported success-rate improvements, undermining the empirical claim.

    Authors: Full experimental details (environments, 5 random seeds, means and standard deviations) are reported in Section 5 and the appendix. The abstract summarizes only the headline result. We will append a short clause noting the number of runs and that improvements are reported with standard deviations. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained

full rationale

The abstract and available description claim a PAC-Bayes bound and MDL-based segmentation cost added to the GRPO objective, but supply no equations, fitting procedures, or self-citations that reduce any prediction to its inputs by construction. No load-bearing step can be quoted or shown to collapse into a fitted parameter or prior self-result. The central claim therefore remains independent of the inspected text and is consistent with external benchmarks (ALFWorld, TextWorld, Countdown) rather than forced by internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no equations, parameters, or background assumptions; ledger entries cannot be populated.

pith-pipeline@v0.9.1-grok · 5658 in / 1157 out tokens · 21853 ms · 2026-06-28T22:46:10.932447+00:00 · methodology

discussion (0)

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Reference graph

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