Retrospective Progress-Aware Self-Refinement for LLM Agent Training
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Computer Science > Computation and Language
Title:Retrospective Progress-Aware Self-Refinement for LLM Agent Training
Abstract:LLM-based agents trained with reinforcement learning optimize step-wise action prediction but lack metacognitive awareness of task progress, inducing a gap that hinders long-horizon scaling. A pilot study reveals that online progress prompting hurts performance while retrospective demonstrations help, yet this capability cannot emerge from outcome-reward training alone. We present RePro, Retrospective Progress-Aware Training, a framework that trains agents to self-generate progress signals via a forward-then-reflect rollout paradigm: the agent executes actions online, then retrospectively reassesses its step-wise progress given the completed trajectory and known outcome. RePro initializes with a Retrospection Warmup that teaches reflection format from minimal external demonstrations, then further trains through RePro-PO with a composite reward that produces self-generated signals without continuous external supervision. Experiments on WebShop, ALFWorld, and Sokoban show that RePro enhances the Qwen family's performance, with up to $12\%$ absolute success rate gains.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.14302 [cs.CL] |
| (or arXiv:2606.14302v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14302
arXiv-issued DOI via DataCite (pending registration)
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