Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Tong, Mei, Jincheng, Dai, Hanjun, Wen, Zixin, Cen, Shicong, Schuurmans, Dale, Chi, Yuejie, Dai, Bo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Beyond Expectations: Learning with Stochastic Dominance Made Practical
by: Cen, Shicong, et al.
Published: (2024)

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF
by: Cen, Shicong, et al.
Published: (2024)

Stochastic Gradient Succeeds for Bandits
by: Mei, Jincheng, et al.
Published: (2024)

Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
by: Yang, Tong, et al.
Published: (2023)

Ordering-based Conditions for Global Convergence of Policy Gradient Methods
by: Mei, Jincheng, et al.
Published: (2025)

Autoregressive Large Language Models are Computationally Universal
by: Schuurmans, Dale, et al.
Published: (2024)

Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning
by: Dai, Bo, et al.
Published: (2026)

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
by: Mei, Jincheng, et al.
Published: (2025)

Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RL
by: Yang, Tong, et al.
Published: (2025)

Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games
by: Yang, Tong, et al.
Published: (2025)

Scalable LLM Reasoning Acceleration with Low-rank Distillation
by: Dong, Harry, et al.
Published: (2025)

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation
by: Lin, Max Qiushi, et al.
Published: (2025)

Spectral Representation-based Reinforcement Learning
by: Gao, Chenxiao, et al.
Published: (2025)

Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning
by: Zhang, Hongming, et al.
Published: (2023)

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
by: Huang, Yu, et al.
Published: (2024)

UQE: A Query Engine for Unstructured Databases
by: Dai, Hanjun, et al.
Published: (2024)

Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function Approximation
by: Che, Fengdi, et al.
Published: (2024)

Exploring and Benchmarking the Planning Capabilities of Large Language Models
by: Bohnet, Bernd, et al.
Published: (2024)

Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
by: Huang, Yu, et al.
Published: (2025)

Diffusion Controller: Framework, Algorithms and Parameterization
by: Yang, Tong, et al.
Published: (2026)

Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
by: Li, Gen, et al.
Published: (2023)

Delightful Gradients Accelerate Corner Escape
by: Mei, Jincheng, et al.
Published: (2026)

Representation Learning via Non-Contrastive Mutual Information
by: Guo, Zhaohan Daniel, et al.
Published: (2025)

AmorLIP: Efficient Language-Image Pretraining via Amortization
by: Sun, Haotian, et al.
Published: (2025)

Prompt-prompted Adaptive Structured Pruning for Efficient LLM Generation
by: Dong, Harry, et al.
Published: (2024)

Agentic Transformers Provably Learn to Search via Reinforcement Learning
by: Yang, Tong, et al.
Published: (2026)

Plastic Learning with Deep Fourier Features
by: Lewandowski, Alex, et al.
Published: (2024)

Large Language Models can Learn Rules
by: Zhu, Zhaocheng, et al.
Published: (2023)

Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
by: Shi, Laixi, et al.
Published: (2022)

Multi-head Transformers Provably Learn Symbolic Multi-step Reasoning via Gradient Descent
by: Yang, Tong, et al.
Published: (2025)

In-Context Learning with Representations: Contextual Generalization of Trained Transformers
by: Yang, Tong, et al.
Published: (2024)

Variational Best-of-N Alignment
by: Amini, Afra, et al.
Published: (2024)

The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards
by: Huang, Yu, et al.
Published: (2026)

The Sample-Communication Complexity Trade-off in Federated Q-Learning
by: Salgia, Sudeep, et al.
Published: (2024)

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs
by: Li, Changhao, et al.
Published: (2024)

Toward Understanding In-context vs. In-weight Learning
by: Chan, Bryan, et al.
Published: (2024)

Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
by: Liu, Songtao, et al.
Published: (2024)

FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
by: Dai, Ben-Ao, et al.
Published: (2025)

Communication-Efficient Federated Optimization over Semi-Decentralized Networks
by: Wang, He, et al.
Published: (2023)

BOND: Aligning LLMs with Best-of-N Distillation
by: Sessa, Pier Giuseppe, et al.
Published: (2024)