mlsanityData to 14 Oct 2026

Limitations of refinement methods for weak to strong generalization

arXiv 2508.17018 ↗First posted 23 Aug 2025stat.ML cs.LGcited by 1

On the map: LLM alignment / RLHF / preference optimisation → LLM preference optimization methods

Abstract (arXiv)

Standard techniques for aligning large language models (LLMs) utilize human-produced data, which could limit the capability of any aligned LLM to human level. Label refinement and weak training have emerged as promising strategies to address this superalignment problem. In this work, we adopt probabilistic assumptions commonly used to study label refinement and analyze whether refinement can be outperformed by alternative approaches, including computationally intractable oracle methods. We show that both weak training and label refinement suffer from irreducible error, leaving a performance gap between label refinement and the oracle. These results motivate future research into developing alternative methods for weak to strong generalization that synthesize the practicality of label refinement or weak training and the optimality of the oracle procedure.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in LLM preference optimization methods, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 29 of its 41 references that are in the map. First posted on arXiv 23 Aug 2025.

Builds on

29 of 41 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model24 Oct
  2. 2024Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models29 May
  3. 2022Training language models to follow instructions with human feedback4 Mar

Other references · 26

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  3. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 467
  4. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 366
  5. 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 310
  6. 2018Marginal Singularity, and the Benefits of Labels in Covariate-Shiftcited by 65
  7. 2019Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifiercited by 65
  8. 2024Theoretical Analysis of Weak-to-Strong Generalizationcited by 48
  9. 2024Quantifying the Gain in Weak-to-Strong Generalizationcited by 41
  10. 2019Convergence Rates for Gaussian Mixtures of Expertscited by 39
  11. 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Expertscited by 24
  12. 2024On Least Square Estimation in Softmax Gating Mixture of Expertscited by 23
14 more
  1. 2024Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalizationcited by 23
  2. 2024Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedurecited by 22
  3. 2024Weak-to-Strong Generalization Through the Data-Centric Lenscited by 21
  4. 2020Minimax optimal approaches to the label shift problem in non-parametric settingscited by 20
  5. 2024Transcendence: Generative Models Can Outperform The Experts That Train Themcited by 20
  6. 2021A linear adjustment based approach to posterior drift in transfer learningcited by 17
  7. 2024Provable Weak-to-Strong Generalization via Benign Overfittingcited by 16
  8. 2018Identifiability of Nonparametric Mixture Models and Bayes Optimal Clusteringcited by 15
  9. 2023Transformers can optimally learn regression mixture modelscited by 15
  10. 2024A transfer learning framework for weak-to-strong generalizationcited by 14
  11. 2022Adapting to Latent Subgroup Shifts via Concepts and Proxiescited by 12
  12. 2024On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusioncited by 9
  13. 2024Generalizing Trust: Weak-to-Strong Trustworthiness in Language Modelscited by 6
  14. 2020A Computationally Efficient Classification Algorithm in Posterior Drift Model: Phase Transition and Minimax Adaptivitycited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026On the Blessing of Pre-training in Weak-to-Strong Generalizationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Improving Weak-to-Strong Generalization with Reliability-Aware Alignment
  2. 2024Your Weak LLM is Secretly a Strong Teacher for Alignment
  3. 2024Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
  4. 2025Contrastive Weak-to-strong Generalization
  5. 2024Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning
  6. 2024Bayesian WeakS-to-Strong from Text Classification to Generation
  7. 2026When Weak LLMs Speak with Confidence, Preference Alignment Gets Stronger
  8. 2025Large Language Models Do Multi-Label Classification Differently