arxiv preprint - World Model on Million-Length Video And Language With RingAttention

In this episode, we discuss World Model on Million-Length Video And Language With RingAttention by Hao Liu, Wilson Yan, Matei Zaharia, Pieter Abbeel. The paper discusses the creation of large-scale transformers trained on extended video and language sequences, introducing methods such as RingAttention to manage the training of models with context sizes up to 1M tokens. Solutions like masked sequence packing and loss weighting are proposed to handle the challenges in vision-language training, and the paper presents highly optimized implementations for these techniques. Notably, the authors have open-sourced a suite of models with 7B parameters capable of processing long sequences of both text and video data, thereby enhancing AI's understanding of human language and the physical world.