14 papers · 1 filter
Muown: Row-Norm Control for Muon Optimization
Kai Lion, Florian Hübler, Bingcong Li +2
Muon has emerged as a strong competitor to AdamW for language model pre-training, yet its behavior at scale is sensitive to weight decay. Recent work has observed that, for Muon wi…
FedVSSAM: Mitigating Flatness Incompatibility in Sharpness-Aware Federated Learning
Bingnan Xiao, Yuan Gao, Bingcong Li +3
Sharpness-aware minimization (SAM) is an effective method for improving the generalization of federated learning (FL) by steering local training toward flat minima. Under data hete…
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2
Test-time compute scaling, the practice of spending extra computation during inference via repeated sampling, search, or extended reasoning, has become a powerful lever for improvi…
Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation
Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li +1
Meta-learning offers a principled framework leveraging \emph{task-invariant} priors from related tasks, with which \emph{task-specific} models can be fine-tuned on downstream tasks…
Scalable Variational Bayesian Fine-Tuning of LLMs via Orthogonalized Low-Rank Adapters
Haotian Xiang, Bingcong Li, Qin Lu
When deploying large language models (LLMs) to safety-critical applications, uncertainty quantification (UQ) is of utmost importance to self-assess the reliability of the LLM-based…
Zeroth-Order Optimization at the Edge of Stability
Minhak Song, Liang Zhang, Bingcong Li +3
Zeroth-order (ZO) methods are widely used when gradients are unavailable or prohibitively expensive, including black-box learning and memory-efficient fine-tuning of large models,…