Miner uses intrinsic policy uncertainty with token-level focal credit assignment and adaptive advantage calibration as a self-supervised reward to enable efficient RL training on positive homogeneous prompts, yielding up to 4.58 Pass@1 gains over GRPO on Qwen3 models.
We only maintain health and biology subsets for testing medical reason- ing abilities, which includes 1535 problems
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Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
Miner uses intrinsic policy uncertainty with token-level focal credit assignment and adaptive advantage calibration as a self-supervised reward to enable efficient RL training on positive homogeneous prompts, yielding up to 4.58 Pass@1 gains over GRPO on Qwen3 models.