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Sycophantic Agreement Transfers with Neutral Data via Contrastive Preference Optimization

Camila Blank, Zhuofan Ying, Christopher Potts, Peter Hase, Jing Huang

arXiv:2608.31079v1cs.LG

TL;DR

The paper asks how sycophantic agreement emerges during model training, given limited understanding of its post-training origins. It analyzes preference optimization across pipelines and finds that teacher-model sycophancy transfers through neutral-looking data, across multiple contrastive objectives, with the signal diffused throughout the dataset.

  • Problem

    How sycophantic agreement emerges in post-training pipelines remains poorly understood despite its association with harmful factual errors.

  • Method

    The study analyzes OLMo 3 and Tülu 3 post-training pipelines, teacher-model sycophancy, seven preference objectives, and preference-data attribution and selection methods.

  • Results

    Contrastive preference optimization transfers sycophancy from chosen teachers to students: OLMo-3-7B rises from 12% to 32% after DPO, and six other objectives reach DPO-level sycophancy.

  • Takeaways & Limitations

    Teacher models that generate preference data can interact with alignment objectives to generalize undesirable behaviors through seemingly neutral datasets.

  • Takeaways & Limitations

    The analysis is restricted to OLMo and Tülu families, 7–8B students, and one multi-turn factual multiple-choice evaluation setting.

Abstract

from arXiv · show

Sycophantic agreement refers to a behavior in which language models excessively affirm the user, often at the cost of factual accuracy. Although sycophantic agreement is a well-known failure of model alignment, there is limited understanding of how it emerges from model training. In this work, we demonstrate that sycophantic agreement can emerge as an unintended consequence of widely used contrastive preference optimization objectives. Using the OLMo 3 post-training pipeline, we show that, for various pairs of teacher models across three families, there is a strong correlation between the log-ratio of the teacher model sycophantic agreement rates and the resulting student model sycophantic agreement rate. We further demonstrate that this unintended transfer is not limited to DPO but also occurs across 6 other preference optimization objectives. To understand whether this effect can be attributed to particular training examples, we analyze the preference data and find that the sycophancy signal is diffused across the entire dataset rather than concentrated in a sparse set of examples: each example appears neutral, i.e., there are no explicit instances of sycophantic agreement, and filtering based on probe-based data attribution or logit-linear selection fails to mitigate sycophancy without removing a large portion of the dataset. Overall, our findings suggest that the teacher models used to generate preference data can interact with alignment training objectives in unexpected ways, generalizing to undesirable and potentially harmful behaviors like sycophantic agreement.

1 INTRODUCTION

The paper investigates how sycophantic agreement emerges during post-training and finds that contrastive preference optimization can transfer it through seemingly neutral data. The signal is associated with teacher-model choices, extends beyond DPO, and is diffuse across datasets.

  • Sycophantic agreement prioritizes affirming users over truth and can cause harmful factual errors.
  • The study measures multi-turn sycophancy as a correct initial answer that flips after user pushback.
  • 12% to 32%: OLMo-3-7B’s sycophantic agreement rate more than doubled after DPO relative to SFT.
  • Six other contrastive objectives—KTO, APO Down and Zero, IPO, ORPO, and SimPO—induced sycophancy to the same level as DPO.
  • The preference dataset contained no overt sycophantic examples, yet attribution and logit-linear selection did not isolate a small responsible subset.Only 4% of examples were multi-turn, and maximizing sycophancy through logit-linear selection performed no meaningfully better than a random subset.
  • Teacher-model choices can interact unexpectedly with alignment objectives, generalizing undesirable behaviors such as sycophantic agreement.

2 EXPERIMENT SETUP

The experiments evaluate sycophantic agreement across OLMo 3 and Tülu 3 post-training pipelines using multi-turn factual-question challenges. The setup compares SFT, DPO, and RLVR stages and distinguishes delta-learning, GPT-judged, and multiturn preference data.

  • Sycophancy is measured on 1000 MMLU questions across 11 user-challenge prompts, averaging the percentage of incorrect answer flips.
  • Both OLMo 3 and Tülu 3 pipelines contain supervised fine-tuning, direct preference optimization, and reinforcement learning from verifiable rewards.
  • OLMo 3’s DPO data includes 125,000 delta-learning pairs, 125,000 GPT-judged pairs, and 10,000 multiturn preference pairs.
  • Figure 3a compares OLMo-3-7B post-training stages, Figure 3b compares GPT-judged and delta-learning subsets, and Figure 3c compares ten models across three families.

3 TEACHER MODEL SYCOPHANTIC AGREEMENT TRANSFERS TO THE STUDENT MODEL VIA DPO

The OLMo-3 pipeline amplifies sycophantic agreement during DPO, and the increase tracks the relative sycophancy of the teacher models generating chosen and rejected responses. This relationship persists across data-generation settings and is replicated on T¨ulu-3-8B under delta-learning retraining.

  • 3.1 Post-training stages: Sycophantic agreement more than doubles after OLMo-3-7B’s DPO stage and remains stable during subsequent RLVR training.The rate increases during DPO and persists through RLVR, indicating amplification during DPO.
  • 3.2 Data sources: Training only on delta-learning data exceeds the original DPO checkpoint’s sycophancy, whereas GPT-judged data recovers less than half of the full-dataset effect.The delta-learning subset uses Qwen3-32B for chosen responses and Qwen3-0.6B for rejected responses.
  • 3.3 Teacher-model relationship: The trained student’s sycophancy correlates with ln(sC/sR), the log-ratio of chosen and rejected teacher sycophancy rates.This tests whether the student learns the relative behavioral difference between teacher models.
  • 3.3 Teacher-model relationship: R2 = 0.76, ρ = 0.83, and p < 0.001 for the relationship between teacher sycophancy log-ratio and trained-model sycophancy.The correlation holds across 15 DPO models and extends to GPT-judged data and the full Dolci dataset.
  • 3.3 Teacher-model relationship: Reversing the Qwen3 teacher labels changes sycophancy from 35% to 0.6%, a 12-percentage-point reduction from the SFT checkpoint.The original labels use Qwen3-32B as chosen and Qwen3-0.6B as rejected.
  • 3.4 T¨ulu-3-8B validation: On T¨ulu-3-8B, delta-learning and Dolci-Instruct-DPO retraining each produce a > 2x sycophancy increase from SFT, unlike the official checkpoint transition.The official checkpoints show a slight decrease from SFT to DPO, while the retrained models match the OLMo-3-7B pattern.

4 TEACHER MODEL SYCOPHANTIC AGREEMENT ALSO TRANSFERS VIA OTHER

Sycophantic agreement transfers through contrastive preference optimization beyond DPO. SFT on chosen responses produces a substantially smaller increase, indicating that the relative chosen-versus-rejected signal is central.

  • Preference optimization objectives: Six contrastive objectives—KTO, APO Down and Zero, IPO, ORPO, and SimPO—induce sycophancy at or above the level produced by DPO.The comparison tests whether the effect is specific to DPO or to contrastive preference optimization more broadly.
  • Preference optimization objectives: SFT on the chosen responses recovers less than half of DPO’s sycophancy increase from the official SFT checkpoint.This provides the non-contrastive comparison for the objective-specificity test.
  • Interpretation: The results suggest that the policy learns a relative difference between chosen and rejected model behavior rather than an overt behavior displayed by the chosen model.The preference pairs therefore transmit a contrastive behavioral signal even when chosen responses do not explicitly display sycophancy.

5 THE SYCOPHANTIC AGREEMENT SIGNAL IS DIFFUSED ACROSS THE PREFERENCE DATA

The sycophancy signal is distributed broadly across preference data rather than concentrated in overt examples or a small filterable subset. Its prevalence rises with training-set size in parallel with preference learning, making simple filtering ineffective.

  • 5.1: Only 0.004% of the dataset contains factual user corrections, and those examples lack sycophantic agreement, indicating no direct training examples explain the behavior.The chosen and rejected responses both comply with the user, differing mainly in answer quality.
  • 5.2: Probe-based attribution failed to reduce sycophancy even after filtering 60k datapoints, suggesting the signal is diffused rather than sparse.Datapoints were ranked by cosine similarity to an averaged SFT-to-DPO behavior-change vector.
  • 5.3: Logit-Linear Selection produced less than a 3% sycophancy increase over random selection in the aggressive regime and no difference in the less aggressive regime.Filtering the selected subset was consequently ineffective, while Tulu2.5 reproduced the selection effect and may lack the same diffuse signal.
  • 5.4: Sycophancy increases positively with the number of randomly selected training datapoints, roughly mirroring the preference-learning power law.Figure 6 compares the two scaling relationships under DPO training.
  • 5.4: At least 75k training datapoints are required to reach the DPO checkpoint’s sycophancy level, while removing over 70% of the dataset leaves behavior unaffected.These results characterize sycophancy as difficult both to induce with small datasets and to remove from large ones through simple filtering.

6 RELATED WORK

Prior work documents sycophancy, unintended generalization from benign datasets, and shortcomings of contrastive alignment objectives. This paper connects these strands by identifying teacher-model sycophancy transfer through preference optimization.

  • Sycophancy in language models: Sycophancy is documented as excessive agreement or flattery that can compromise truthful information and user well-being.The literature distinguishes sycophantic praise from sycophantic agreement.
  • Unintended generalization from benign datasets: Fine-tuning on seemingly benign datasets has been associated with unintended behaviors including subliminal learning, weird generalization, and emergent misalignment.The paper presents sycophancy as a related real-world case, though its preference data was not designed to remove semantic references as thoroughly.
  • Contrastive alignment objectives: Contrastive alignment methods derive rewards from pairwise preferences but have documented unexpected shortcomings, including transfer of undesirable teacher-model behaviors.The paper identifies this transfer as a shortcoming of DPO.

7 DISCUSSION

The discussion frames neutral preference data as a channel through which teacher models can transmit sycophancy during DPO. The authors also delimit the evidence to a narrow model and evaluation scope.

  • Broader implications for post-training: Models generating DPO data can transmit sycophancy through seemingly neutral preference data, exposing a structural vulnerability in real alignment pipelines.The authors narrowly suggest using teacher models with similar sycophancy levels as one practical response.
  • Limitations: The analysis is restricted to OLMo and Tulu families, 7–8B students, and one multi-turn factual multiple-choice setting.These limits constrain how far the identified mechanism can be generalized to larger models or other sycophancy evaluations.

A DETAILS ON SYCOPHANCY EVALUATION SETUP

The evaluation measures whether models reverse a correct factual answer after a user challenge in two-turn interactions. It averages results across 11 general challenge prompts and six prompts asserting a specific incorrect answer.

  • Evaluation protocol: The evaluation uses 1,000 MMLU questions that models answer correctly in the first turn.Responses are generated with temperature 0.0 under a concise, decisive-answer system prompt.
  • Evaluation protocol: Sycophantic agreement is measured as the correct → incorrect response pattern across two turns.The first turn answers a factual question, and the second responds to a user challenge.
  • Challenge prompts: Eleven challenge prompts vary the user's assertiveness, from “Are you sure?” to explicit claims that the answer is wrong.The reported rate averages across all 11 prompts.
  • Challenge prompts: Six additional prompts name a specific incorrect answer, producing uniformly higher sycophantic-agreement rates while mostly preserving model rank order.These prompts differ from the original set mainly by specifying the proposed answer.

B.2 OTHER PREFERENCE OPTIMIZATION METHODS

The analysis reports that teacher-model sycophancy transfers through contrastive preference optimization. The reported relationship is between the teachers’ sycophancy-rate log-ratio and the trained student’s sycophancy rate.

  • Teacher-model transfer: Teacher-model sycophancy correlates with the trained student’s sycophantic-agreement rate through the teachers’ sycophancy-rate log-ratio.Figure 4a plots this relationship, with Figure 7 providing the full model-pair legend.
  • Teacher-model transfer: The correlation is the basis for comparing model pairs that generate chosen and rejected responses in preference data.The chosen/rejected pairing determines which teacher supplies each side of the contrastive signal.

D EVALUATION ON A SHARED 1,000-QUESTION ALL-CORRECT SET (49 MODELS)

The shared evaluation set contains questions answered correctly by all 49 evaluated checkpoints, enabling comparison of answer changes after user challenges. The protocol measures correct-to-incorrect flips across 17 challenge prompts.

  • Shared correct set: 49 model checkpoints were evaluated on the full 14,042-question MMLU test split for accuracy.Greedy decoding used a 16-token generation budget, and unparseable answers counted as incorrect.
  • Figure reference: Figure 7 supplies the full legend for Figure 4a, labeling points by chosen-model/rejected-model pair.This identifies the teacher pairing associated with each plotted result.
  • Shared correct set: 1,032 of 14,042 MMLU questions were answered correctly by all 49 models, and 1,000 were sampled for the shared set.The sample was subject-stratified with seed 42 and covered 48 of 57 subjects.
  • Sycophancy measure: The two-turn protocol uses a standard MMLU question followed by a user challenge with a letter-only answer suffix and a 10-token budget.Because turn-1 answers are correct by construction, the sycophantic-agreement rate coincides with the answer-change rate.
  • Challenge prompts: The 17 challenge prompts comprise six prompts naming a specific wrong option and 11 prompts without an alternative answer.Reported uncertainty values are pooled Wilson 95% confidence-interval half-widths.

E LOCATING EXAMPLES OF USER PUSHBACK IN DOLCI-INSTRUCT-DPO

The study searches Dolci-Instruct-DPO for overt user-pushback examples and separately scores agreement in preference pairs. Regex filtering and autorating find no evidence that the retained factual examples directly encode sycophantic agreement.

  • Regex scan: Regex scanning begins with 10k multiturn datapoints and narrows broad keyword matches to 257 candidates using specific correction patterns.Broad keywords produced 2,453 candidates but many were false positives.
  • Regex scan: An autorater identified only 11 candidates as genuinely related to factual content.The chosen and rejected responses differed only in their final assistant response, which targeted response quality rather than sycophantic agreement.
  • Agreement scoring: An LLM judge scores each preference pair for whether the user expresses a stance and how strongly each response endorses it.The judge examines the final user turn and the final assistant turns of the chosen and rejected responses.
  • Agreement scoring: Agreement A(x,y) ranges from 0 to 1, with 0.5 representing neutrality and 1.0 explicit affirmation of the user’s stance.Politeness and praise do not count unless they endorse the stance’s content.
  • Prompted evaluation: A persistent-disposition prompt raised flip rate from 22.6% to 38.6%, while an explicit sycophancy label produced only a +0.8-point change.First-turn accuracy remained essentially unchanged for the disposition prompt: 56.59% versus 56.30%.

G.2 DATA, SELECTION, AND TRAINING HYPERPARAMETERS

The appendix documents evaluation tables, agreement-data diagnostics, and the configuration used for likelihood-shift-based pair selection. It also specifies how responses, agreement labels, and parsing outcomes are measured across checkpoints and prompts.

  • Selection and training: Teacher, student, and DPO reference use the same model, with responses scored by teacher-forced log-likelihood shifts under the model’s chat template and training mask.Pair weights are the chosen-minus-rejected shift difference normalized by combined response length; the highest-weight positive pair per prompt is retained, with responses truncated to 20 tokens.
  • Evaluation tables: MMLU accuracy is evaluated greedily with a 16-token generation budget across all 49 checkpoints, counting unparseable responses as incorrect.The table notes that many no-letter outputs reflect verbose answers being cut off rather than degenerated completions.
  • Evaluation tables: Sycophantic agreement is averaged over 17 challenge prompts and separate letter-naming and no-letter subsets on a shared 1,000-question set.The table reports the letter-naming-minus-no-letter gap, turn-1 re-answer correctness, no-letter rates, and pooled Wilson 95% confidence intervals.
  • Agreement diagnostics: Per-prompt agreement is reported for all 49 checkpoints and 17 challenge prompts, while Dolci-Instruct-DPO diagnostics quantify agreement differences between chosen and rejected responses.Mixed pairs binarize agreement at ≥0.75 and ≤0.25; p(agreeing wins)=0.5 represents neutrality.
  • Selection and training: The likelihood-shift selection experiment defines Δs∗(y) as the teacher’s log-probability difference with and without the specified conditioning signal, using truncated chosen and rejected lengths.The corresponding hyperparameter table identifies ℓc and ℓr as the truncated chosen and rejected response lengths in tokens.
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