Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

📰 ArXiv cs.AI

Learn how to refine large language models using dynamic one-shot policy refinement for resource-efficient reinforcement learning

advanced Published 5 May 2026

Action Steps

Apply dynamic one-shot policy refinement to existing reinforcement learning frameworks
Configure the refinement process to adapt to changing reward signals
Test the refined policy on a range of reasoning tasks to evaluate performance
Compare the results with traditional reinforcement learning methods to assess efficiency gains
Refine the model further by incorporating additional reward signals and rollout data

Who Needs to Know This

Researchers and engineers working on large language models can benefit from this technique to improve model performance while reducing computational costs. This can be particularly useful for teams working on complex reasoning tasks

Key Insight

💡 Dynamic one-shot policy refinement can significantly reduce the computational costs associated with reinforcement learning for large language models

Key Takeaways

Learn how to refine large language models using dynamic one-shot policy refinement for resource-efficient reinforcement learning

Full Article

Title: Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement

Abstract:
arXiv:2602.00815v2 Announce Type: replace Abstract: Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards (RLVR) emerging as a principled framework for aligning model behavior with reasoning chains. Despite its promise, RLVR remains prohibitively resource-intensive, requiring extensive reward signals and incurring substantial rollout costs during training. In this work, we revisit the fundamental questi

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