7 papers
AdaBoN: Adaptive Best-of-N Alignment
Vinod Raman, Hilal Asi, Satyen Kale
Recent advances in test-time alignment methods, such as Best-of-N sampling, offer a simple and effective way to steer language models (LMs) toward preferred behaviors using reward…
Faster Rates For Federated Variational Inequalities
Guanghui Wang, Satyen Kale
In this paper, we study federated optimization for solving stochastic variational inequalities (VIs), a problem that has attracted growing attention in recent years. Despite substa…
Understanding Outer Optimizers in Local SGD: Learning Rates, Momentum, and Acceleration
Ahmed Khaled, Satyen Kale, Arthur Douillard +3
Modern machine learning often requires training with large batch size, distributed data, and massively parallel compute hardware (like mobile and other edge devices or distributed…
Eager Updates For Overlapped Communication and Computation in DiLoCo
Satyen Kale, Arthur Douillard, Yanislav Donchev
Distributed optimization methods such as DiLoCo have been shown to be effective in training very large models across multiple distributed workers, such as datacenters. These method…
Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch
Arthur Douillard, Yanislav Donchev, Keith Rush +11
Training of large language models (LLMs) is typically distributed across a large number of accelerators to reduce training time. Since internal states and parameter gradients need…
Efficient Stagewise Pretraining via Progressive Subnetworks
Abhishek Panigrahi, Nikunj Saunshi, Kaifeng Lyu +4
Recent developments in large language models have sparked interest in efficient pretraining methods. Stagewise training approaches to improve efficiency, like gradual stacking and…