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PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

Research LLM Agents

Merged summary

TL;DR - PATS is an adaptive training scaffold for long-horizon LLM agents that turns recent rollout evidence into task-specific guidance, then removes redundant support as the policy improves. It boosts agent performance while reducing prompt-token usage.

  • Converts rollout groups from the latest policy into evidence cards that guide subsequent attempts.
  • Dynamically revises or removes guidance to prevent overreliance while preserving rollout diversity.
  • Uses standard RLVR with environmental rewards; the scaffold is discarded at deployment.
  • Improves ALFWorld and WebShop results by up to 18.6% and uses 32.1% fewer prompt tokens across seven search-augmented QA benchmarks than the baseline.

Sources (1)

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

arXiv cs.AI Yipeng Shi, Zhipeng Ma, Yue Wang, Qitai Tan, Yang Li, Peng Chen, Zhengzhou Zhu 2026-07-23 arXiv:2607.21419

TL;DR - PATS is an adaptive training scaffold for long-horizon LLM agents that turns recent rollout evidence into task-specific guidance, then removes redundant support as the policy improves. It boosts agent performance while reducing prompt-token usage.

  • Converts rollout groups from the latest policy into evidence cards that guide subsequent attempts.
  • Dynamically revises or removes guidance to prevent overreliance while preserving rollout diversity.
  • Uses standard RLVR with environmental rewards; the scaffold is discarded at deployment.
  • Improves ALFWorld and WebShop results by up to 18.6% and uses 32.1% fewer prompt tokens across seven search-augmented QA benchmarks than the baseline.
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