5 citations · 6 across the 11 of their papers we have counts for
14 papers
Provable Test-Time Scaling for Beam Search in LLM Reasoning
Qijia He, Yu Huang, Yuan Cheng +2
Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning paths early,…
A lower bound for stepsize-based acceleration of gradient descent
Jianhao Ma, Yuxin Chen
Recent work has shown that, for smooth convex optimization, plain gradient descent can be accelerated from its textbook convergence rate of (where denotes the numbe…
Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions
Yuepeng Yang, Yuxin Chen, Yuejie Chi
Distributionally robust Markov decision processes provide a principled framework for sequential decision making under model uncertainty. We study how many samples are necessary and…
WSqD: A Horizon-Free Learning Rate Schedule for Large Model Training
Jianhao Ma, Yuxin Chen
Standard learning rate schedules such as cosine annealing are tied to a fixed training horizon, limiting their ability to accommodate post hoc horizon extension. Warmup-stable-deca…
On the Emergence of Implicit Curriculum in RLVR Learning Dynamics
Yu Huang, Zixin Wen, Yuejie Chi +4
Reinforcement learning with verifiable rewards (RLVR) has been a main driver of recent breakthroughs in large reasoning models. Yet it remains a mystery how rewards based solely on…
Preconditioning Benefits of Spectral Orthogonalization in Muon
Jianhao Ma, Yu Huang, Yuejie Chi +1
The Muon optimizer, a matrix-structured algorithm that leverages spectral orthogonalization of gradients, is a milestone in the pretraining of large language models. However, the u…
Transformers Provably Learn Chain-of-Thought Reasoning with Length Generalization
Yu Huang, Zixin Wen, Aarti Singh +2
The ability to reason lies at the core of artificial intelligence (AI), and challenging problems usually call for deeper and longer reasoning to tackle. A crucial question about AI…
Statistical and Algorithmic Foundations of Reinforcement Learning
Yuejie Chi, Yuxin Chen, Yuting Wei
As a paradigm for sequential decision making in unknown environments, reinforcement learning (RL) has received a flurry of attention in recent years. However, the explosion of mode…
Transformers Meet In-Context Learning: A Universal Approximation Theory
Gen Li, Yuchen Jiao, Yu Huang +2
Large language models are capable of in-context learning, the ability to perform new tasks at test time using a handful of input-output examples, without parameter updates. We deve…
Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models
Yuchen Wu, Yuxin Chen, Yuting Wei
Diffusion models play a pivotal role in contemporary generative modeling, claiming state-of-the-art performance across various domains. Despite their superior sample quality, mains…
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality
Zhihan Huang, Yuting Wei, Yuxin Chen
The denoising diffusion probabilistic model (DDPM) has emerged as a mainstream generative model in generative AI. While sharp convergence guarantees have been established for the D…
A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
Gen Li, Yuting Wei, Yuejie Chi +1
Diffusion models, which convert noise into new data instances by learning to reverse a diffusion process, have become a cornerstone in contemporary generative modeling. In this wor…
Accelerating Convergence of Score-Based Diffusion Models, Provably
Gen Li, Yu Huang, Timofey Efimov +3
Score-based diffusion models, while achieving remarkable empirical performance, often suffer from low sampling speed, due to extensive function evaluations needed during the sampli…
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
Tong Yang, Shicong Cen, Yuting Wei +2
Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a mult…