13 papers
Sharper Analysis of Single-Loop Methods for Bilevel Optimization
Yubo Zhou, Jun Shu, Luo Luo +4
Bilevel optimization underpins many machine learning applications, including hyperparameter optimization, meta-learning, neural architecture search, and reinforcement learning. Whi…
Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
Chengli Tan, Yubo Zhou, Haishan Ye +7
Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone…
ESSAM: A Novel Competitive Evolution Strategies Approach to Reinforcement Learning for Memory Efficient LLMs Fine-Tuning
Zhishen Sun, Sizhe Dang, Guang Dai +1
Reinforcement learning (RL) has become a key training step for improving mathematical reasoning in large language models (LLMs), but it often has high GPU memory usage, which makes…
On the Convergence of Single-Loop Stochastic Bilevel Optimization with Approximate Implicit Differentiation
Yubo Zhou, Luo Luo, Guang Dai +1
Stochastic Bilevel Optimization has emerged as a fundamental framework for meta-learning and hyperparameter optimization. Despite the practical prevalence of single-loop algorithms…
Riemannian Momentum Tracking: Distributed Optimization with Momentum on Compact Submanifolds
Jun Chen, Tianyi Zhu, Haishan Ye +5
Gradient descent with momentum has been widely applied in various signal processing and machine learning tasks, demonstrating a notable empirical advantage over standard gradient d…
From to : Two-Sided Low-Rank Communication for Adam in Distributed Training with Memory Efficiency
Sizhe Dang, Jiaqi Shao, Xiaodong Zheng +3
As foundation models continue to scale, pretraining increasingly relies on data-parallel distributed optimization, making bandwidth-limited gradient synchronization a key bottlenec…