Resist, Update, Reject: Preference Optimization Installs a Prior-Dependent Reliability Switch
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
arXiv:2609.22359 (cs)
[Submitted on 26 Jul 2026]
Title:Resist, Update, Reject: Preference Optimization Installs a Prior-Dependent Reliability Switch
View a PDF of the paper titled Resist, Update, Reject: Preference Optimization Installs a Prior-Dependent Reliability Switch, by Sen Yang and Yuen-Hei Yeung
View PDF
HTML (experimental)
Abstract:An aligned model asked to hold its answer against a manipulative source must still update on a reliable one and reject an unreliable one: resistance, reliable-update, and unreliable-source rejection are one three-way contract, not three independent behaviors. We show the objective most anti-sycophancy work optimizes is non-identifying with respect to source reliability: because no preference label depends on whether a source is actually reliable, any scalar mixture of the arms traces a single deference dial, and no point separates two same-template testimonies differing only in stated reliability. This fixation$\leftrightarrow$gullibility frontier is a property of the objective, not any model. We make reliability identifiable through data: a threshold benchmark where a source asserts the opposite answer while stating its reliability $r$, and the correct action is to flip iff $r$ exceeds the model's prior strength $p$. Preference optimization over balanced coverage installs a prior-dependent reliability switch: across three seeds on Qwen2.5-7B-Instruct the threshold $r^\star$ rises monotonically with the prior, decision accuracy reaches $0.84$ with a monotone flip curve (Spearman $0.56$), and the policy generalizes to unseen reliability values and a held-out notation, following stated reliability over role prestige. Three controls localize the cause: an unmatched variant installs the switch equally ($0.80$), a second preference optimizer (IPO) installs it just as well ($0.86$), whereas supervised imitation does not ($0.50$), so the cause is preference optimization over reliability-labeled coverage, not pairing, loss, or imitation. A confirmatory battery replicates the switch on a fresh test draw, bounds it honestly (it keys on reliability stated in the testimony, not a separately audited record), and transfers it to Llama-3.1-8B. The frontier is empirical, not a theorem.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.22359 [cs.LG] |
| (or arXiv:2609.22359v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22359
arXiv-issued DOI via DataCite (pending registration)
|
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
View a PDF of the paper titled Resist, Update, Reject: Preference Optimization Installs a Prior-Dependent Reliability Switch, by Sen Yang and Yuen-Hei Yeung
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Stable and Faithful Explanations for Knowledge Tracing
Sep 25
-
SMILESGNN: Interpretable Clinical Toxicity Prediction via SMILES-Graph Cross-Attention Fusion
Sep 25
-
CFD Correction of Open Tip Clearance Flow in a Compressor Cascade Using VAE Latent Space Adaptation
Sep 25
-
CARE: Condition-Aware Representation Regularization for Diffusion Models
Sep 25
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.