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 ex
The paper addresses a critical limitation of current LLM agent training methodologies, specifically the lack of metacognitive awareness of task progress, which is becoming more apparent as LLM applications scale.
This research offers a pathway to more robust and scalable AI agents by improving their ability to self-monitor and refine actions, crucial for complex, long-horizon tasks.
The introduction of Retrospective Progress-Aware Training (RePro) enables LLM agents to generate and leverage internal progress signals, moving beyond simple outcome-reward optimization.
- · AI development platforms
- · Robotics
- · SaaS companies leveraging AI agents
- · Companies using LLM agents for complex workflows
- · AI agent approaches focused solely on outcome-based reinforcement learning
LLM agents become more efficient and capable of handling multi-step tasks.
This improved reliability accelerates the deployment of AI agents into broader commercial applications.
More sophisticated and autonomous AI agents could further collapse white-collar workflows and necessitate new human-AI collaboration paradigms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL