The Development of Sycophancy in Language Models: Tracing Vulnerabilities from Pretraining to Post-Training
How to prepare an accurate paper press kit? Illustrated guide
Haonan Huang, Chengkun Cai · arXiv: 2610.26655v1 · Summary page
Why cover this story this week?
Tracing the roots of AI agreement
When a model abandons an answer to accommodate a user’s opinion, without receiving new evidence, researchers call the behavior sycophancy. A preprint follows 741 training checkpoints from 30 open model lineages, testing responses to moral scenarios and subsequent user pressure. In most lineages, the tendency was already present before post-training. Its average strength could rise or fall at different stages, with no single trajectory across models.
The more revealing pattern concerned which particular questions a released model yielded on. Those vulnerabilities became much more predictable during post-training, especially after preference optimization. A checkpoint from the model’s own training route was substantially more informative than a sibling model trained from the same base along another route. Two models can therefore have similar average sycophancy scores while giving way on different questions. Understanding a released model’s weaknesses may require its training history, not just a single headline score.
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