EDGE: Experience-Distillation for Guided Exploration in Agentic Reinforcement Learning

2026-08-22

AI Summary

本文提出EDGE框架，通过经验蒸馏和引导探索解决强化学习中经验复用问题，提升策略性能并减少对外部检索依赖。

Abstract

Reinforcement learning with outcome-based objectives such as GRPO enables LLM-based agents to solve complex, long-horizon tasks, yet the reusable exploration patterns embedded in interaction trajectories are largely discarded after a single policy update. Existing experience-augmented approaches retrieve historical guidance at inference time, but they apply experiences without accounting for the policy's evolving capability and create persistent dependencies on external retrieval. We propose EDGE (Experience-Distillation for Guided Exploration), a framework that treats retrieved experiences as temporary training-time scaffolds and progressively internalizes their benefits into the parametric policy. Concretely, EDGE partitions each rollout group into experience-conditioned and experience-free trajectories to estimate and admit only positive marginal gains without extra sampling, then distills the induced behavior into the base policy via a reverse-KL objective on its own empirical support. A co-evolutionary experience bank further synthesizes guidance from emerging failure modes and prunes obsolete entries as the policy evolves. On ALFWorld and WebShop, EDGE improves over GRPO by 8.3 and 12.5 success-rate points at the 7B scale and retains 96.0% of its scaffolded performance when external experiences are removed at inference time. The code is available at https://github.com/xvolcano02/EDGE.

Problem

• Reinforcement Learning
• Experience Augmentation
• Policy Evolution

Innovation

• Experience-Distillation
• Guided Exploration
• reverse-KL objective
• co-evolutionary experience bank