arXiv:2601.11960v3 Announce Type: replace-cross Abstract: Existing reinforcement learning methods for LLM reasoning implicitly assume that the policy generating training trajectories should coincide with the one producing inference responses. We argue that this is a misleading inductive bias: the optimization-optimal trajectory distribution favors informative gradients, whereas the inference-optimal response distribution emphasizes accuracy and consistency. Forcing both into a single policy entangles their gradients and suppresses exploration. We propose R$^2$PO (Residual Rollout Policy Optimi
This paper addresses a fundamental limitation in current LLM reasoning methods, pushing for more efficient and effective reinforcement learning techniques as LLM capabilities expand.
Improving LLM reasoning and exploration capabilities directly impacts the reliability and performance of AI agents and sophisticated AI applications, accelerating practical deployment.
The proposed R$^2$PO method changes how LLMs are trained for reasoning, potentially leading to more accurate, consistent, and generalizable AI models.
- · AI model developers
- · Companies deploying AI agents
- · Academic AI research
- · Users of advanced AI applications
- · Developers relying on sub-optimal LLM training paradigms
- · Companies with less sophisticated AI research capabilities
LLMs trained with R$^2$PO will demonstrate enhanced reasoning abilities and reduced training inefficiencies.
More reliable and capable AI agents could accelerate automation in complex cognitive tasks, impacting white-collar workflows.
The broader adoption of advanced reasoning techniques could lead to faster breakthroughs in scientific discovery and problem-solving through AI augmentation.
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Read at arXiv cs.AI