5 papers
Accelerating LLM Pre-Training through Flat-Direction Dynamics Enhancement
Shuchen Zhu, Rizhen Hu, Mingze Wang +4
Pre-training Large Language Models requires immense computational resources, making optimizer efficiency essential. The optimization landscape is highly anisotropic, with loss redu…
Non-Asymptotic Global Convergence of PPO-Clip
Yin Liu, Qiming Dai, Junyu Zhang +1
Reinforcement learning has gained attention for modern Large Language Model post-training. The actor-only variants of Proximal Policy Optimization (PPO) are widely applied for thei…
A Memory Efficient Randomized Subspace Optimization Method for Training Large Language Models
Yiming Chen, Yuan Zhang, Yin Liu +2
The memory challenges associated with training Large Language Models (LLMs) have become a critical concern, particularly when using the Adam optimizer. To address this issue, numer…
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures
Yiming Chen, Yuan Zhang, Liyuan Cao +2
Parameter-efficient fine-tuning (PEFT) significantly reduces memory costs when adapting large language models (LLMs) for downstream applications. However, traditional first-order (…
Sharper Convergence Guarantees for Federated Learning with Partial Model Personalization
Yiming Chen, Liyuan Cao, Kun Yuan +1
Partial model personalization, which encompasses both shared and personal variables in its formulation, is a critical optimization problem in federated learning. It balances indivi…