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Locating Sycophancy Across Training Stages in Open Language Models

Jiarui Yao, Shweta Parihar, Nghia Tuan Bui, Guergana K Savova, Lijing Wang

arXiv:2610.37788v1cs.CLcs.AI

TL;DR

The paper asks where sycophancy originates, given conflicting reports and the fact that answer-change rates mix suggestion influence with initial firmness. It tracks open-model checkpoints across training stages and finds distinct origins for prompt deference and conversational capitulation.

  • Problem

    Studies disagree about sycophancy’s training-stage origins, and answer-change rates mix a suggestion’s influence with the model’s initial answer firmness.

    Source: §1 · p. 2

  • Method

    The study measures answer movement and initial firmness across 13 open checkpoints and four multiple-choice datasets, varying claim source and strength in prompts and follow-up messages.

    Source: §1 · p. 2

  • Results

    Prompt deference is present in every base model, while conversational capitulation is largely absent in base models and appears in every post-trained checkpoint.

    Source: §1 · p. 2, §5 · p. 9

  • Takeaways & Limitations

    Separating suggestion push from initial firmness prevents low answer-change rates from being mistaken for resistance to influence when they reflect firmness alone.

    Source: §4 · p. 9, §5 · p. 10

    The study reads a single answer-slot token on multiple-choice items and tests only two-turn conversations, so open-ended tasks and longer exchanges remain outside its scope.

    Source: §4 · p. 9

Abstract

from arXiv · show

Language model assistants sometimes change a correct answer when the user suggests a wrong one. The behavior, called sycophancy, is usually attributed to preference-based post-training, yet early measurements found it in pretrained models. We ask where it comes from by measuring it at every available stage of training: 13 open checkpoints from 5 lineages, from base model through supervised fine-tuning and preference optimization to release. We separate how far an injected claim moves a model's answer from how firmly the answer was held, and vary who makes the claim, how reliable they are said to be, and how strongly they make it. Two sycophancy behaviors with different origins emerge, and both replicate across datasets. Deference to a claim in the prompt is already present in every base model, which ranks sources in the same order as its post-trained descendants. Capitulation in conversation, dropping a stated answer when the user objects, is largely absent from base models and present in every post-trained checkpoint. When the objection names an alternative, most post-trained models give up firmly held answers as often as loosely held ones. We discuss the implications of these measurements for evaluating sycophancy and approaches for addressing each behavior.

1 INTRODUCTION

Prior studies disagree about where sycophancy originates, partly because answer changes combine a suggestion’s influence with the model’s initial firmness. This paper separates those behaviors and finds that deference to claims in prompts and capitulation during conversation have different training-stage origins.

1 INTRODUCTION

  • Prior studies place sycophancy variously in pretraining, instruction tuning, or preference optimization, leaving its training-stage origins disputed.The studies also differ in settings and measurements, including prompt claims versus conversational pushback.Source: §1 · pp. 1–2
  • Change rates conflate how much a suggestion moves an answer with how firmly the model held it, so lower rates alone do not establish weaker influence.Post-training can increase answer firmness, making a model change less often under the same push without weighing the claim less.Source: §1 · p. 2
  • The study tracks 13 open checkpoints across training stages and measures both the movement caused by claims and the firmness of initial answers.It varies the claim’s speaker, stated reliability, and strength, and tests both prompt claims and follow-up objections.Source: §1 · p. 2
  • Prompt deference is present in every base model, while conversational capitulation is largely absent before post-training and appears in every post-trained checkpoint.When an objection names an alternative, most post-trained models give up firmly held answers nearly as readily as loosely held ones.Source: §1 · p. 2

2 METHOD

The method compares answer-slot probabilities across bare and suggested multiple-choice prompts and across checkpoints, separating initial answer firmness from the effect of a suggestion. It also compares suggestions at equal initial firmness.

2 METHOD

  • The design varies suggestion, checkpoint, and placement, reading the model’s next-token probabilities over answer letters at a fixed answer slot.The 13 checkpoints span base, SFT, DPO, and released stages; suggestions vary speaker, credibility, medium, stance, and strength.Source: §2 · pp. 2–3

2.1 SETUP

  • Each item has a gold answer and one uniformly sampled wrong target; paired bare and suggested prompts differ by one inserted line.The suggestion bank includes target-naming templates and a bare-mention control, while both base and chat formats end at the same answer anchor.Source: §2.1 · p. 3, Figure 1 · p. 3, Table 1 · p. 4

2.2 MEASUREMENT: GAP, PUSH, AND FLIP

  • The method distinguishes the model’s initial lead over a suggested target, the suggestion’s push against that lead, and a flip when the target becomes the answer.It analyzes known items where the model initially answers correctly; conversational messages without a named option are measured by abandon rate instead.Source: §2.2 · pp. 4–5

2.3 COMPARING SENTENCES AT EQUAL FIRMNESS

  • Because a flip requires push at least as large as the initial lead, suggestions are compared within quintiles of equal initial firmness.Within each stratum, push and flip rate are averaged over the same items, reducing the risk of reading a larger initial lead as weaker influence.Source: §2.3 · p. 5

3 EXPERIMENTS AND RESULTS

The experiments separate suggestion influence from answer firmness across training stages, revealing that single-turn deference is already present in base models while conversational capitulation emerges with post-training. The section also tests how certainty, source cues, chat templates, and model size relate to these behaviors.

3 EXPERIMENTS AND RESULTS

  • The study evaluates 13 checkpoints from five lineages on four multiple-choice datasets, testing prompts with suggestions that vary in speaker, reliability, and strength.MMLU results appear in the main paper; results on the other three datasets are in the appendix.Source: §3 · p. 5

3.1 FLIP RATE AND FIRMNESS

  • Post-training increases answer leads by about two- to twenty-fold, while suggestion push varies little across certainty strata and flip rates fall sharply from Q1 to Q5.Thus, overall flip rates mostly reflect answer firmness rather than weaker suggestion influence in post-trained checkpoints.Source: §3.1 · pp. 5–6, Figure 3 · p. 6

3.2 THE SINGLE-TURN LADDER IS ALREADY IN THE BASE MODEL

  • Base-model suggestion ladders persist in descendants, while capitulation to a bare challenge is largely absent in bases and appears by supervised fine-tuning.The base and released-model ladders agree on 196 of 200 matched comparisons; capitulation varies by training recipe.Source: §3.2 · p. 6, §3.3 · p. 8
  • Across datasets, bare mentions prime the named option, while post-training adds a small discount for flagged sources and recipe-specific credit for credible sources.A bare mention yields between half and all of the professor’s push, and a third to three quarters of the strongest sentence’s push.Source: §3.2 · p. 7, Figure 5 · p. 7

3.3 IN CONVERSATION, POST-TRAINING INSTALLS COMPLIANCE

  • When a turn-2 objection names an alternative, most post-trained models flip on 61–100% of items overall, often with little decline at high certainty.Qwen2.5 Instruct is an exception: its flip rate falls steeply with certainty.Source: §3.3 · p. 8, Figure 6 · p. 8

3.4 TWO CONTROLS: THE CHAT WRAPPER AND MODEL SIZE

  • The chat template neither creates nor removes the behavior; size adds little base-model firmness, while the push and post-training firmness multiplier hold across tested sizes.The size comparison covers Llama-3.1 at 70B and Qwen2.5 at 14B, 32B, and 72B.Source: §3.4 · pp. 8–9

4 DISCUSSION

The paper distinguishes prompt deference, present before post-training, from conversational capitulation, which post-training adds. Separating claim influence from answer firmness clarifies why answer-change rates alone can mislead, while the study leaves the responsible recipe ingredients unresolved.

4 DISCUSSION

  • Prompt deference is present in base models, while conversational capitulation is largely absent from base models and appears in every post-trained checkpoint.The base-model exception is on Sharma’s two-option items; post-trained models capitulate under content-free challenges at every certainty level.Source: §4 · p. 9
  • Because answer changes require a suggestion’s push to exceed the model’s initial lead, greater firmness can look like resistance without reducing influence.A low flip rate can reward firmness and miss conversational compliance; comparing push and turn-2 behavior at matched certainty separates these effects.Source: §4 · p. 9
  • A desirable post-training recipe would retain credible-source weighting, resist bare objections when answers are firm, and scale responses with certainty, but no tested checkpoint has all three properties.The study cannot identify which recipe ingredient produces each property.Source: §4 · p. 9
  • The measurements cover answer-slot logits on multiple-choice items and two-turn conversations, not reasoning-before-answer behavior, open-ended tasks, or longer exchanges.The main experiments use five lineages of 7–8B models.Source: §4 · p. 9

5 CONCLUSION

Across the five examined open-model families, prompt claims are already treated as evidence in base models, while post-training adds conversational capitulation. Separating claim push from answer firmness makes these patterns measurable and prevents firmness alone from being mistaken for resistance.

5 CONCLUSION

  • Base models in the five examined open-model families already treat contextual claims as evidence and weight them by their source.Post-training preserves the ladder’s direction, reorders its middle, and makes models firmer.Source: §5 · p. 9
  • Post-training adds the conversational behavior of giving up a stated answer when the user objects, at a rate unrelated to answer firmness.Separating push from gap also reveals differences among post-training recipes.Source: §5 · pp. 9–10
  • Separating claim push from the answer’s initial gap keeps a low answer-change rate from being mistaken for resistance when it reflects firmness.Source: §5 · p. 10

AI USE STATEMENT

The authors used LLMs for literature search, coding, writing, and discussion, while retaining responsibility for the paper’s hypotheses, design, analyses, and conclusions.

AI USE STATEMENT

  • LLMs assisted with related-work searches, code drafting, prose polishing, and discussion of arguments and potential gaps.The authors read every cited paper and verified the code with direct review, synthetic-data tests, and independent-run checks.Source: AI USE STATEMENT · p. 10
  • The authors wrote the paper’s content and used an LLM to tighten prose and check it against tables and figures.Source: AI USE STATEMENT · p. 10
  • The authors state that the hypotheses, design choices, analyses, and conclusions are their own and that they take full responsibility.Source: AI USE STATEMENT · p. 10

A RELATED WORK

Related work links sycophancy to preference optimization while documenting it in pretrained models, broadening its definitions, and examining both prompt cues and conversational pushback. These different settings and measures provide context for the paper’s stage-resolved comparison.

A RELATED WORK

B THE SENTENCE BANK

The sentence bank organizes suggestions by five framing factors and uses placeholders for the endorsed answer. It also includes bare, follow-up, challenge, and negated forms.

B THE SENTENCE BANK

  • Suggestions vary across speaker, credibility, medium, stance, and strength, with {X} or {T} marking the endorsed answer.Unnamed cells B0, B1, MT0, and MT1 contain no letter and yield one row per item.Source: Table 2 · p. 15
  • The bank includes a neutral request for a final answer and a bare challenge asking whether the model is sure.These are turn-two follow-ups rather than suggestions naming an answer option.Source: B THE SENTENCE BANK · p. 16
  • Templates include plain, expert, stranger, forceful, and desire-based statements, with affirmative and negated versions.Examples include a professor or stranger endorsing or denying an answer, and a speaker expressing hope for it.Source: B THE SENTENCE BANK · p. 16

C MODEL ACCURACY

The model-accuracy section reports bare-question accuracy for checkpoints in their native formats across four multiple-choice datasets, including MMLU sampling across 57 subjects.

C MODEL ACCURACY

D RESULTS ON THE OTHER DATASETS

The section’s supplied visual caption describes four-dataset summaries of answer stability and target-option probability in native formats; the available result heading identifies overall flip rate.

D RESULTS ON THE OTHER DATASETS

  • Figure 7 summarizes the median runner-up gap and median p0(âi) over all items across four datasets in native format.Source: Figure 7 · p. 17
  • The supplied result heading identifies overall flip rate across base, SFT, DPO, and released checkpoints, including Llama-3.1 Instruct.Source: D RESULTS ON THE OTHER DATASETS · p. 18

OLMo-2

The OLMo-2 figures extend earlier figure formats to named datasets, while the logit-readout and generation-check tables describe generated-row outcomes for affirmative and negated cells.

OLMo-2

  • The captions identify ARC-Challenge, TruthfulQA MC1, Sharma, and TruthfulQA, presenting those datasets in formats corresponding to Figures 4 and 5; another caption follows Figure 3 across all datasets.Source: Figure 8 · p. 18, Figure 9 · p. 19, Figure 10 · p. 20

E LOGIT READOUT AND GENERATION CHECK

  • Table 4 reports match, mismatch, and unparsed generated-row shares by checkpoint, format, and dataset, pooled over affirmative cells and both targets.The remainder of generated rows are ties.Source: Table 4 · p. 23
  • Table 5 reports match, mismatch, and unparsed shares for six negated cells and their affirmative twins.Source: Table 5 · p. 24

F TWO CONTROLS IN FULL: THE CHAT WRAPPER AND MODEL SIZE

The chat wrapper does not create or remove sycophantic behavior, though its effects vary by model and can change source rankings. Increasing model size adds little firmness to base models and does not reduce suggestion-induced push.

F TWO CONTROLS IN FULL: THE CHAT WRAPPER AND MODEL SIZE

  • The chat wrapper neither creates nor removes the behavior, but it reshuffles source rankings most for Qwen2.5 base (rank correlation 0.55–0.62).No instruct model loses its push without the wrapper; Qwen2.5 and Mistral Instruct are firmer in chat, while Llama-3.1 Instruct, T¨ulu-3, and OLMo-2 Instruct are similarly firm in plain and chat formats.Source: F TWO CONTROLS IN FULL: THE CHAT… · p. 24
  • Base-model firmness stays between 2 and 5 logits from 7B to 72B, while post-training’s firmness multiplier holds at every size.A 70B Llama-3.1 base is less firm than an 8B Instruct model: 3.5 versus 5.0 logits in plain format, and 2.1 versus 5.9 in chat.Source: F TWO CONTROLS IN FULL: THE CHAT… · p. 24
  • Suggestion-induced push does not shrink with size: plain user sentences move Llama-3.1 by 2–4 logits at both sizes and Qwen2.5 base by 1.4–2.8 logits at every size.The largest Qwen2.5 Instruct pushes occur at 32B and 72B.Source: F TWO CONTROLS IN FULL: THE CHAT… · p. 24
  • With size, weight shifts toward the expert: the user’s edge over the professor falls from +0.75 to +0.13 logits for Llama-3.1 base and from +0.5 to −0.5 for Qwen2.5 base.Qwen2.5 Instruct shifts from +0.25 to −1.8 logits; the user’s insistence premium shows no size trend.Source: F TWO CONTROLS IN FULL: THE CHAT… · p. 24
  • Qwen2.5 14B Instruct’s plain-format user-sentence push is 0.75 logits, against a 10.4-logit lead.Its push averages 0.9 logits over cells.Source: F TWO CONTROLS IN FULL: THE CHAT… · p. 25

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