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)
// (it isn't always in every impl)
Россия нарастила до максимума вывоз одного лакомства08:43，更多细节参见电影
cargo install plyx
，这一点在谷歌浏览器【最新下载地址】中也有详细论述
Солнце выбросило гигантский протуберанец размером около миллиона километров02:48
音頻加註文字，哈梅內伊：統治伊朗37年的最高領袖是誰？。业内人士推荐快连下载安装作为进阶阅读