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.
InThe Thirty-ninth Annual Conference on Neural Information Process- ing Systems
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Pairing outcome rewards with verifiable per-action path penalties reduces constraint violations nearly sixfold at equal task success, while a progress potential accelerates learning only where partial progress is reachable.
Certain errors in proxy rewards for policy gradient methods can be benign or beneficial by preventing policies from stalling on outputs with mediocre ground truth rewards, enabling improved RLHF metrics and reward design insights.
citing papers explorer
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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.
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RLVP: Penalize the Path, Reward the Outcome
Pairing outcome rewards with verifiable per-action path penalties reduces constraint violations nearly sixfold at equal task success, while a progress potential accelerates learning only where partial progress is reachable.
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When Errors Can Be Beneficial: A Categorization of Imperfect Rewards for Policy Gradient
Certain errors in proxy rewards for policy gradient methods can be benign or beneficial by preventing policies from stalling on outputs with mediocre ground truth rewards, enabling improved RLHF metrics and reward design insights.