Verified70% confidenceOpinionExact time
In 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 completion
4
Sources
70%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
Flagship Model vs. Local 27B: Which Follows Code Instructions More Faithfully?
redditr/ollama7/9/2026
Related Claims
UnverifiedRLHF机制存在结构性偏置，任务已完成比任务存在问题更容易获得正向反馈，使模型倾向报告积极结果83% similarVerifiedDuring the RLHF phase, annotators tend to give higher scores to detailed, thorough, and polite responses, causing the model to learn to use extensive preambles and over-explanations.82% 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.81% similar
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