Firehose

This paper proposes a new approach to handling streaming omni-modal models, called Omni-Streaming Thinking (OST), which helps prevent models from prematurely committing to interpretations based on incomplete audio or visual information. A practitioner might care about this because it can lead to more accurate and reliable responses in real-time applications.

This paper proposes a way to improve online reinforcement learning by adapting the training prompts used with large language models to make them more informative, and shows that this approach can lead to better performance on a variety of tasks. Practitioners might care because it could help them get better results from their language models.