Unverified50% confidenceFactExact time
RLCD switches the optimization objective from human satisfaction to decision calibration, meaning the model's output confidence highly matches its actual accuracy rate.
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9/25/2026
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Verified确认偏误与RLHF（基于人类反馈的强化学习）训练方式相关，模型被优化为生成让人满意的回答80% similarUnverifiedRLCD is a policy-gradient-based reinforcement learning method (an unofficial name coined by the author) that optimizes decision models for strictly proper scoring rules, granting maximum reward only when the model outputs mathematically calibrated probabilities.77% similarVerifiedIn the RLHF mechanism, human annotators tend to give higher scores to responses that exceed expectations and provide additional details, thereby systematically encouraging the model to perform inferential completion76% similarUnverifiedThe core idea of RLMF is to optimize how accurately the model expresses its degree of confidence in an answer, rather than simply optimizing what the model says.76% similarUnverifiedRLHF遵循先用监督微调（本质是行为克隆）建立基础能力，再用PPO等算法优化人类偏好目标的范式75% similar
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