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arXiv · 2605.19447

What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents

Xiaozhe Li·Tianyi Lyu·Yang Li·Yichuan Ma·Peiji Li·Linyang Li·Qipeng Guo·Dahua Lin·Kai Chen
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Abstract

Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a single success-or-failure signal must be distributed across many actions. Existing methods rely on trajectory-level rewards or proxy signals, without fully leveraging per-step environmental feedback. Multi-turn agent settings are underexplored, where feedback can include error messages, page changes, observations, or reference trajectories. We systematically study five feedback sources and two insertion granularities and introduce SERL, a selective environment-reweighted learning framework. SERL uses the task reward to determine update direction, while environment feedback adjusts placement and magnitude, focusing on critical actions. On ALFWorld and WebShop, SERL achieves 90.0% and 80.1% success, outperforming strong RL and distillation baselines. Analysis shows that grounded, action-relevant feedback at meaningful points consistently outperforms indiscriminate use of longer or richer context.

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Xiaozhe Li, Tianyi Lyu, Yang Li, Yichuan Ma, Peiji Li, Linyang Li, Qipeng Guo, Dahua Lin, Kai Chen. 2026-05-19. What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents. https://arxiv.org/abs/2605.19447

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