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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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Source

Plain-Language Brief

The authors propose a new training paradigm called PATS for reinforcement learning agents that learn long-term tasks. PATS converts rollout groups from the latest policy into evidence cards and adjusts the context used in subsequent rollouts based on task-specific evaluation. This helps weak policies to complete challenging tasks, with improvements seen in ALFWorld and WebShop environments, as well as on search-augmented QA benchmarks where it uses fewer prompt tokens than the baseline.

What the Document Says

  • The authors propose a new policy-centric training paradigm named PATS.
  • PATS converts rollout groups from the latest policy into evidence cards.
  • Task-specific evaluation is used to adjust the context in subsequent rollouts.
  • Concrete guidance helps weak policies complete challenging tasks.
  • As policy improves, redundant context is revised or removed.
  • The training scaffold is discarded at deployment.
  • PATS shows improvements on ALFWorld and WebShop environments.
  • On search-augmented QA benchmarks, PATS remains competitive while using fewer prompt tokens than the baseline.

Key Concepts Mentioned

  • reinforcement learning
  • long-horizon tasks
  • policy optimization
  • skill-centric methods
  • dynamic training scaffold
  • PATS
  • ALFWorld
  • WebShop
  • search-augmented QA benchmarks

Claims Made by the Document

  • Weak policies often repeat similar failures in long-horizon LLM agent reinforcement learning.
  • Existing skill-centric methods improve exploration by optimizing, filtering, or internalizing reusable skills.
  • Our proposed policy-centric training paradigm improves over strong baselines on ALFWorld and WebShop environments.

Evidence Used

  • Improvements up to 18.6% on ALFWorld and WebShop
  • Remains competitive on search-augmented QA benchmarks while using 32.1% fewer prompt tokens than the baseline

Methods / Approach

  • policy-centric training paradigm
  • evidence cards
  • task-specific evaluation
  • standard RLVR

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The study does not provide a comparison with other policy-centric training methods beyond existing skill-centric ones.
  • It is unclear how PATS performs on tasks outside of ALFWorld, WebShop, and search-augmented QA benchmarks.

Open Questions

  • How does PATS compare to other policy-centric training methods?
  • How does PATS perform on tasks outside of the tested environments?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide a comparison with other policy-centric training methods beyond existing skill-centric ones. Additionally, it is unclear how PATS performs on tasks outside of ALFWorld, WebShop, and search-augmented QA benchmarks.

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.