Applied Compute

This talk introduces an efficient reinforcement learning (RL) approach for training large language models, focusing on making the process faster and cheaper for enterprise use cases. The core idea is to break the synchronous dependency between sampling data and training the model, enabling asynchronous operations. This method aims to improve ROI by moving AI beyond simple productivity tasks into real-world automations.