Verified80% confidenceFactTime unknown
大语言模型在RLHF训练阶段，因人类标注员倾向于给更详细、更礼貌的回答打高分，导致模型学会了过度冗长的表达模式
13
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80%
Confidence
Long-term
Relevance
6/1/2026
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UnverifiedDuring 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.84% similarUnverified大语言模型的指令跟随能力是通过RLHF训练习得的统计倾向，注意力机制无法从架构层面区分硬规则与软建议83% similarUnverified不同大语言模型因训练数据分布、RLHF偏好对齐策略和模型架构差异，在同一问题上呈现风格迥异的回应倾向80% similarUnverified当前语言模型广泛采用RLHF（基于人类反馈的强化学习）训练范式，同样无法免疫规范博弈这一挑战79% similar
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