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Query-Efficient Rejection Sampling for Parallel Masked Diffusion
Shiyuan Zhang, Xuheng Li, Qiwei Di +1
Masked diffusion models (MDMs) accelerate generation by unmasking multiple tokens in parallel, but sampling these tokens independently ignores their correlations and biases the out…
Muon Learns Facts Better: Understanding the Role of Spectral Orthogonalization
Xuheng Li, Qiwei Di, Yuan Cao +1
The Muon optimizer applies spectral orthogonalization to matrix-valued updates and has shown strong performance in large-scale neural network training, yet the mechanisms of this t…
Understanding Off- vs On-Policy Distillation: A Tale of Distinct Training Objectives
Qiwei Di, Xuheng Li, Kaixuan Ji +3
On-policy distillation (OPD) learns from teacher feedback on student-generated responses and has shown promise in reducing forgetting relative to supervised fine-tuning (SFT). Howe…
Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
Kaixuan Ji, Qiwei Di, Qingyue Zhao +2
We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers. For -armed band…
Saturation-Insensitive Dueling Bandits with General Function Approximation
Chenggong Zhang, Xuheng Li, Qiwei Di +2
We study contextual dueling bandits with general function approximation under the Bradley-Terry-Luce (BTL) preference model. A key challenge in this setting is the saturation of th…
On the Optimal Sample Complexity of Offline Multi-Armed Bandits with KL Regularization
Kaixuan Ji, Qiwei Di, Heyang Zhao +2
Kullback-Leibler (KL) regularization is widely used in offline decision-making and offers several benefits, motivating recent work on the sample complexity of offline learning with…
Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence
Shiyuan Zhang, Qiwei Di, Xuheng Li +1
Underdamped Langevin dynamics (ULD) is a widely-used sampler for Gibbs distributions , and is often empirically effective in high dimensions. However, existing non…
Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
Kaixuan Ji, Qingyue Zhao, Heyang Zhao +2
Recent studies have shown that reinforcement learning with KL-regularized objectives can enjoy faster rates of convergence or logarithmic regret, in contrast to the classical $\sqr…