Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models

• URL: http://arxiv.org/abs/2601.04731v1
• Date: Thu, 08 Jan 2026 08:52:37 GMT
• Title: Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
• Authors: Shuyang Jiang, Yuhao Wang, Ya Zhang, Yanfeng Wang, Yu Wang,
• Abstract summary: Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts.<n>We introduce a radically simple yet powerful solution to ulineMine ulineintrinsic mastulineery (Miner)<n>Miner repurposes the policy's intrinsic uncertainty as a self-supervised reward signal.
• Score: 40.61814017829362
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting in waste of rollouts due to zero advantage estimates. We introduce a radically simple yet powerful solution to \uline{M}ine \uline{in}trinsic mast\uline{er}y (Miner), that repurposes the policy's intrinsic uncertainty as a self-supervised reward signal, with no external supervision, auxiliary models, or additional inference cost. Our method pioneers two key innovations: (1) a token-level focal credit assignment mechanism that dynamically amplifies gradients on critical uncertain tokens while suppressing overconfident ones, and (2) adaptive advantage calibration to seamlessly integrate intrinsic and verifiable rewards. Evaluated across six reasoning benchmarks on Qwen3-4B and Qwen3-8B base models, Miner achieves state-of-the-art performance among the other four algorithms, yielding up to \textbf{4.58} absolute gains in Pass@1 and \textbf{6.66} gains in Pass@K compared to GRPO. Comparison with other methods targeted at exploration enhancement further discloses the superiority of the two newly proposed innovations. This demonstrates that latent uncertainty exploitation is both necessary and sufficient for efficient and scalable RL training of reasoning models.

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