mlsanityData to 14 Oct 2026

Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment

arXiv 2410.20727 ↗First posted 28 Oct 2024cs.LG stat.MLcited by 9

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

Abstract (arXiv)

Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algorithm is prohibitively expensive in practice due to the sample and computation inefficiency. This paper addresses the problem by revealing a unified game-theoretic connection between iterative BOND and self-play alignment, which unifies seemingly disparate algorithmic paradigms. Based on the connection, we establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization that approximates iterative BOND in the parameter space. We provides provable sample efficiency guarantee for one of the WIND variant with the square loss objective. The experimental results confirm that our algorithm not only accelerates the computation, but also achieves superior sample efficiency compared to existing methods.

Key facts. Cited by 9 papers 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 48 of its 62 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

48 of 62 references.

Closest ancestors · 10

Same direction, newest first

  1. 2024Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization18 Jul
  2. 2024Variational Best-of-N Alignment8 Jul
  3. 2024BoNBoN Alignment for Large Language Models and the Sweetness of Best-of-n Sampling2 Jun
  4. 2024SimPO: Simple Preference Optimization with a Reference-Free Reward23 May
  5. 2024Asymptotics of Language Model Alignment2 Apr
  6. 2024Generalized Preference Optimization: A Unified Approach to Offline Alignment8 Feb
  7. 2024Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation16 Jan
  8. 2023A General Theoretical Paradigm to Understand Learning from Human Preferences18 Oct
  9. 2022Training language models to follow instructions with human feedback4 Mar
  10. 2021A General Language Assistant as a Laboratory for Alignment1 Dec

Other references · 38

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  7. 2017Deep reinforcement learning from human preferencescited by 3,912
  8. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
  9. 2020Learning to summarize from human feedbackcited by 2,588
  10. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  11. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,095
  12. 2023AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedbackcited by 748
26 more
  1. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignmentcited by 686
  2. 2023LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusioncited by 564
  3. 2016Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Lojasiewicz Conditioncited by 539
  4. 2023RRHF: Rank Responses to Align Language Models with Human Feedback without tearscited by 463
  5. 2023SLiC-HF: Sequence Likelihood Calibration with Human Feedbackcited by 401
  6. 2020Loss landscapes and optimization in over-parameterized non-linear systems and neural networkscited by 233
  7. 2024Self-Play Preference Optimization for Language Model Alignmentcited by 232
  8. 2023Nash Learning from Human Feedbackcited by 224
  9. 2024Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferencescited by 178
  10. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 144
  11. 2024Theoretical guarantees on the best-of-n alignment policycited by 124
  12. 2017Stability and Generalization of Learning Algorithms that Converge to Global Optimacited by 100
  13. 2024BOND: Aligning LLMs with Best-of-N Distillationcited by 76
  14. 2024Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHFcited by 73
  15. 2024REBEL: Reinforcement Learning via Regressing Relative Rewardscited by 69
  16. 2022A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Gamescited by 66
  17. 2021Fast Policy Extragradient Methods for Competitive Games with Entropy Regularizationcited by 63
  18. 2022Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policiescited by 46
  19. 2024Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learningcited by 45
  20. 2021Stability and Deviation Optimal Risk Bounds with Convergence Rate O(1/n)cited by 42
  21. 2024In-Context Learning with Representations: Contextual Generalization of Trained Transformerscited by 38
  22. 2024Transforming and Combining Rewards for Aligning Large Language Modelscited by 25
  23. 2023On the Convergence of Encoder-only Shallow Transformerscited by 13
  24. 2023Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learningcited by 11
  25. 2022Symmetric (Optimistic) Natural Policy Gradient for Multi-agent Learning with Parameter Convergencecited by 8
  26. 2022Sharper Utility Bounds for Differentially Private Modelscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025Inference-Time Reward Hacking in Large Language Modelscited by 34
  2. 2026LaViDa-R1: Advancing Reasoning for Unified Multimodal Diffusion Language Modelscited by 7
  3. 2026Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillationcited by 5
  4. 2025Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Gamescited by 3
  5. 2026Reward Learning from Best-of-N Preference Data: Targets, Tradeoffs, and Design Principlescited by 1
  6. 2025Bootstrapping LLMs via Preference-Based Policy Optimizationcited by 0
  7. 2026Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Rewardcited by 0
  8. 2026Exploration-Driven Optimization for Test-Time Large Language Model Reasoningcited by 0
  9. 2026Truncate Bad, Upweight Good: BoN-Style Distillation via Rank-Based Classificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models
  2. 2024Sample-Efficient Alignment for LLMs
  3. 2025Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment
  4. 2025AlignDistil: Token-Level Language Model Alignment as Adaptive Policy Distillation
  5. 2025Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching
  6. 2024COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
  7. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
  8. 2025RSPO: Regularized Self-Play Alignment of Large Language Models