4 papers
Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling
Alexandru Meterez, Depen Morwani, Jingfeng Wu +3
Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining. While for SGD, doubling the batch size can…
On the Collapse Errors Induced by the Deterministic Sampler for Diffusion Models
Yi Zhang, Zhenyu Liao, Jingfeng Wu +1
Despite the widespread adoption of deterministic samplers in diffusion models (DMs), their potential limitations remain largely unexplored. In this paper, we identify collapse erro…
A Simplified Analysis of SGD for Linear Regression with Weight Averaging
Alexandru Meterez, Depen Morwani, Costin-Andrei Oncescu +3
Theoretically understanding stochastic gradient descent (SGD) in overparameterized models has led to the development of several optimization algorithms that are widely used in prac…
How Does Critical Batch Size Scale in Pre-training?
Hanlin Zhang, Depen Morwani, Nikhil Vyas +5
Training large-scale models under given resources requires careful design of parallelism strategies. In particular, the efficiency notion of critical batch size (CBS), concerning t…