Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs
arXiv:2510.04140v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Models (LLMs). However, the effectiveness of RLVR strongly depends on the capability of base models. This issue arises because it requires the model to have sufficient capability to perform high-quality exploration, which involves both effectiveness and diversity. Unfortunately, existing methods address this issue by imitating expert trajectories, which improve effectiveness but neglect divers
The continuous push for more capable and reliable LLMs necessitates advanced reinforcement learning techniques that can overcome current limitations in exploration efficiency and diversity.
Improving RLVR's effectiveness and diversity in LLMs directly impacts the sophistication and safety of AI agents, which are becoming increasingly central to various applications.
This research suggests a more robust method for training LLMs through selective expert guidance, potentially leading to more generally capable and less biased AI models compared to traditional imitation learning techniques.
- · AI developers
- · Large Language Models
- · AI-driven industries
- · Traditional RL methods
- · LLMs with limited reasoning
More robust and versatile large language models will emerge, enhancing autonomous AI capabilities.
Increased adoption of LLM-powered AI agents across various sectors due to improved performance and safety.
This could accelerate the broader societal integration of AI with greater trust in their decision-making processes.
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Read at arXiv cs.CL