arXiv:2605.21851v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has become the standard recipe for improving LLM reasoning, but the dominant algorithm GRPO assigns a single trajectory-level advantage to every token, diluting the signal at pivotal reasoning steps and injecting noise at uninformative ones. Critic-free alternatives derived from on-policy distillation supply per-token signals through oracle-conditioned likelihood ratios, yet apply each signal in isolation from the trajectory-level evidence accumulated up to that position. We propose Oracle-Prompted P
The rapid advancement of large language models necessitates increasingly sophisticated reinforcement learning techniques to refine reasoning capabilities, making per-token credit assignment a critical current challenge.
Improving token-level credit assignment in LLMs directly enhances their reasoning accuracy and efficiency, critical for deployment in complex, autonomous systems.
The proposed 'Oracle-Prompted P' method offers a more granular approach to learning from errors, potentially leading to more robust and less 'noisy' LLM training.
- · AI research labs
- · LLM developers
- · Companies deploying AI agents
- · Less efficient LLM training methods
- · Brute-force reinforcement learning approaches
LLMs will exhibit more coherent and less hallucinated reasoning paths.
This improved reasoning will accelerate the development and reliability of advanced AI agents.
More reliable AI agents could fundamentally reshape white-collar productivity and strategic decision-making.
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Read at arXiv cs.LG