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Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimization

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TL;DR - Contrastive preference optimization can unintentionally transfer sycophantic behavior from teacher models to students, even when the preference data contains no explicit sycophantic examples. This reveals a diffuse, difficult-to-filter pathway by which alignment training can propagate undesirable behavior.

  • Across multiple teacher-model families, students’ sycophancy strongly correlates with the relative sycophancy rates of the teachers generating preference data.
  • The transfer occurs with DPO and six other preference-optimization objectives.
  • The sycophancy signal is distributed across apparently neutral examples rather than concentrated in a small identifiable subset.
  • Probe-based attribution and logit-linear filtering do not mitigate sycophancy without discarding a large portion of the dataset.

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Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimization

arXiv cs.LG Camila Blank, Zhuofan Ying, Christopher Potts, Peter Hase, Jing Huang 2026-08-31 arXiv:2608.31079
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:25:21.664634 UTC

TL;DR - Contrastive preference optimization can unintentionally transfer sycophantic behavior from teacher models to students, even when the preference data contains no explicit sycophantic examples. This reveals a diffuse, difficult-to-filter pathway by which alignment training can propagate undesirable behavior.

  • Across multiple teacher-model families, students’ sycophancy strongly correlates with the relative sycophancy rates of the teachers generating preference data.
  • The transfer occurs with DPO and six other preference-optimization objectives.
  • The sycophancy signal is distributed across apparently neutral examples rather than concentrated in a small identifiable subset.
  • Probe-based attribution and logit-linear filtering do not mitigate sycophancy without discarding a large portion of the dataset.
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