training-techniques

Explores whether very small language models can handle casual conversations adequately, and what training factors differentiate the better ones.

OpenAI presents improved techniques for training consistency models that enable high-quality single-step image generation without distillation, achieving significant FID improvements on CIFAR-10 and ImageNet 64×64 through novel loss functions and training strategies.

OpenAI presents comprehensive techniques for training large neural networks across distributed GPU clusters, covering data parallelism, pipeline parallelism, tensor parallelism, and mixture-of-experts approaches to overcome engineering and scalability challenges.