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Source note3 min read

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.21419v1

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Plain-Language Brief

The paper proposes a new training paradigm called PATS for long-horizon reinforcement learning in language models. The method aims to improve weak policies that repeat similar failures by using a policy-centric approach instead of skill-centric ones. It converts rollout groups from the latest policy into evidence cards and uses task-specific evaluation to adjust context for subsequent rollouts, providing concrete guidance to help weak policies complete challenging tasks. The policy is optimized with environmental rewards using standard RLVR, and the training scaffold is discarded at deployment. PATS shows improvements over strong baselines in ALFWorld and WebShop by up to 18.6%, and it remains competitive while using fewer prompt tokens on seven search-augmented QA benchmarks.

What the Document Says

  • The paper presents a policy-centric training paradigm called PATS for long-horizon reinforcement learning in language models.
  • PATS aims to improve weak policies that repeat similar failures by converting rollout groups from the latest policy into evidence cards and using task-specific evaluation to adjust context for subsequent rollouts.
  • Concrete guidance is provided to help weak policies complete challenging tasks.
  • As policy improves, redundant context is revised or removed to reduce reliance on explicit guidance while preserving useful rollout variation.
  • The policy is optimized with environmental rewards using standard RLVR.
  • The training scaffold is discarded at deployment.
  • On ALFWorld and WebShop, PATS improves over strong baselines by up to 18.6%.
  • Across seven search-augmented QA benchmarks, PATS remains competitive while using 32.1% fewer prompt tokens than the baseline.

Key Concepts Mentioned

  • Long-horizon reinforcement learning
  • Language models
  • Weak policies
  • Policy-centric approach
  • Evidence cards
  • Task-specific evaluation
  • Concrete guidance
  • Redundant context
  • Environmental rewards
  • RLVR
  • ALFWorld
  • WebShop
  • Search-augmented QA benchmarks

Claims Made by the Document

  • PATS improves weak policies in long-horizon reinforcement learning.
  • PATS shows improvements over strong baselines in ALFWorld and WebShop by up to 18.6%.
  • PATS remains competitive while using fewer prompt tokens on seven search-augmented QA benchmarks.

Evidence Used

  • Improvements are observed in experimental results presented in the paper.

Methods / Approach

  • Converting rollout groups into evidence cards and adjusting context for subsequent rollouts.
  • Providing concrete guidance to help weak policies complete challenging tasks.
  • Revising or removing redundant context as policy improves.
  • Optimizing policy with environmental rewards using standard RLVR.

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories and limiting effective policy optimization. Existing skill-centric methods improve exploration by optimizing, filtering, or internalizing reusable skills. However, they remain centered on the skills themselves rather than being designed as adaptive training-time support for the evolving policy. To address this, we propose a policy-centric training paradigm that reframes skills as a dynamic training scaffold. Our framework, Pats, converts rollout groups from the latest policy into evidence cards and uses task-specific evaluation to adjust the context used in subsequent rollouts. Concrete guidance helps weak policies to complete challenging tasks. As policy improves, redundant context is revised or removed to reduce reliance on explicit guidance while preserving useful rollout variation. The policy is optimized with environmental rewards using standard RLVR, and the training scaffold is discarded at deployment. On ALFWorld and WebShop, Pats improves over strong baselines by up to 18.6%. Across seven search-augmented QA benchmarks, it remains competitive while using 32.1% fewer prompt tokens than the baseline.