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NudgeRL framework enhances LLM reasoning via structured exploration

Researchers have developed NudgeRL, a new framework designed to improve the exploration capabilities of reinforcement learning with verifiable rewards (RLVR) for large language models. This method uses "Strategy Nudging" to guide rollouts with lightweight contexts, encouraging diverse reasoning trajectories without needing expensive supervision. NudgeRL has demonstrated superior performance compared to standard methods on five math benchmarks, achieving better results even with significantly larger rollout budgets. AI

影响 Enhances LLM reasoning capabilities by improving exploration efficiency in RLVR, potentially leading to more robust and diverse model outputs.

排序理由 The cluster contains an academic paper detailing a new method for improving LLM reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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NudgeRL framework enhances LLM reasoning via structured exploration

报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sung Ju Hwang ·

    突破舒适区:RLVR的高效策略引导探索

    Reinforcement learning with verifiable rewards (RLVR) has emerged as a scalable paradigm for improving the reasoning capabilities of large language models. However, its effectiveness is fundamentally limited by exploration: the policy can only improve on trajectories it has alrea…