The BrokenMath benchmark (NeurIPS 2025 Math-AI Workshop) tested this in formal reasoning across 504 samples. Even GPT-5 produced sycophantic “proofs” of false theorems 29% of the time when the user implied the statement was true. The model generates a convincing but false proof because the user signaled that the conclusion should be positive. GPT-5 is not an early model. It’s also the least sycophantic in the BrokenMath table. The problem is structural to RLHF: preference data contains an agreement bias. Reward models learn to score agreeable outputs higher, and optimization widens the gap. Base models before RLHF were reported in one analysis to show no measurable sycophancy across tested sizes. Only after fine-tuning did sycophancy enter the chat. (literally)
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収蔵庫改修に2億円！仏像の引っ越しに密着してみた
林俊旸提出离职后，阿里高管紧急答疑。wps对此有专业解读
能力提升是全方位的，可以完整的复述今天在幼儿园一天都做了什么，就算表达有点逻辑颠倒，但引导她顺序以后，能很好的理解并且重新复述。