5 papers
Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training
Zijian Zhang, Rizhen Hu, Athanasios Glentis +4
Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how RL adaptation is distributed across tra…
EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
Chung-Yiu Yau, Dawei Li, Athanasios Glentis +3
Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning training mainly due to stochas…
Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
Athanasios Glentis, Jiaxiang Li, Andi Han +1
Training large language models (LLMs) relies on adaptive optimizers such as Adam, which introduce extra operations and require significantly more memory to maintain first- and seco…
Revisiting the Adam-SGD Gap in LLM Pre-Training: The Role of Large Effective Learning Rates
Athanasios Glentis, Dawei Li, Chung-Yiu Yau +1
It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptive optimizers such as Adam in pre-training Large Language Models (LLMs). Yet th…
Scalable Parameter and Memory Efficient Pretraining for LLM: Recent Algorithmic Advances and Benchmarking
Athanasios Glentis, Jiaxiang Li, Qiulin Shang +4
Fueled by their remarkable ability to tackle diverse tasks across multiple domains, large language models (LLMs) have grown at an unprecedented rate, with some recent models contai…