arXiv:2605.24547v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards can improve LLM reasoning, but learning remains sample-inefficient when terminal rewards are sparse. This has motivated a growing line of work on RL with textual feedback, where a critic model generates natural language feedback to guide a reasoning model (the actor), augmenting scalar rewards with richer learning signals. However, existing methods typically treat feedback as fixed or auxiliary, which misses a key property: feedback should not merely be correct, but should improve the policy (actor m
The increasing sophistication of LLMs and the recognition of sample-inefficiency in traditional RL are driving innovations in feedback mechanisms.
This research could significantly improve the efficiency and effectiveness of training large language models for complex reasoning tasks by leveraging more effective feedback during learning.
The approach shifts from treating textual feedback as auxiliary to optimizing it for policy improvement. This could lead to faster and more robust AI agent development.
- · AI developers
- · LLM researchers
- · AI-driven product companies
- · Traditional RL methods
- · Inefficient AI training paradigms
More sample-efficient training of large language models for advanced reasoning.
Accelerated development of AI agents capable of performing complex, multi-step tasks with less data.
Enhanced automation of white-collar workflows as AI agents become more capable and cost-effective to train.
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Read at arXiv cs.LG