Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.21419v1

Read the source

Source

Plain-Language Brief

The paper 'PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning' proposes a new training paradigm called Pats, which aims to improve the performance of long-horizon reinforcement learning agents with weak policies. Pats presents a policy-centric approach that converts rollout groups from the latest policy into evidence cards and uses task-specific evaluation to adjust the context for subsequent rollouts. The framework improves over strong baselines on ALFWorld and WebShop by up to 18.6%, and it uses fewer prompt tokens across seven search-augmented QA benchmarks while remaining competitive.

What the Document Says

  • The paper proposes a policy-centric training paradigm called Pats for long-horizon reinforcement learning agents.
  • Pats converts rollout groups from the latest policy into evidence cards and uses task-specific evaluation to adjust context for subsequent rollouts.
  • The framework helps weak policies complete challenging tasks by providing concrete guidance.
  • As policy improves, redundant context is revised or removed in Pats.
  • Pats optimizes the policy with environmental rewards using standard RLVR, and discards the training scaffold at deployment.
  • On ALFWorld and WebShop, Pats outperforms strong baselines by up to 18.6%.
  • Across seven search-augmented QA benchmarks, Pats uses 32.1% fewer prompt tokens than the baseline while remaining competitive.

Key Concepts Mentioned

  • reinforcement learning
  • long-horizon LLM agent
  • policy optimization
  • skills
  • training scaffold
  • PATS framework
  • ALFWorld
  • WebShop
  • QA benchmarks

Claims Made by the Document

  • Pats improves the performance of long-horizon reinforcement learning agents with weak policies.
  • Pats outperforms strong baselines on ALFWorld and WebShop by up to 18.6%.

Evidence Used

  • On ALFWorld and WebShop, Pats improves over strong baselines by up to 18.6%.

Methods / Approach

UNKNOWN

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.