aiqbench analysis
This research introduces RePro, a framework that equips LLM agents with metacognitive awareness by training them to retrospectively assess task progress after executing actions. By utilizing a forward-then-reflect paradigm and composite rewards, the method enables self-generated progress signals without continuous external supervision, significantly boosting success rates on complex benchmarks like WebShop and ALFWorld. The study addresses a critical bottleneck in long-horizon agent scaling by proving that outcome-reward training alone is insufficient for developing genuine task-awareness capabilities.
Retrospective Progress-Aware Self-Refinement for LLM Agent Training
arXiv:2606.14302v1 Announce Type: new 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.
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.
Computation and Language (cs.CL)
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)
Full text under the publisher's open licence · 157 words
Related coverage
Retrospective Progress-Aware Self-Refinement for LLM Agent Training
arXiv · Oct 6, 2026
The Continual Constitutional Convention: How language models revise the documents that govern them
AI Alignment Forum · Oct 13, 2026
[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)
Latent Space · Sep 17, 2026
[AINews] not much happened today
Latent Space · Sep 10, 2026