What is an RL environment? w/ Nous Research's Roger Jin

This episode explores the limitations of supervised learning in language modeling and introduces reinforcement learning (RL) as a solution, particularly focusing on the RL infrastructure developed at Noose Research for training open-source language models. Against the backdrop of supervised learning's challenges with non-differentiable loss functions and objectives over multi-step trajectories, the discussion pivots to RL, where an agent interacts with an environment to maximize rewards, which can be arbitrary and weird, offering a more flexible approach. More significantly, the talk highlights how language modeling maps onto RL, with text prefixes as states and next tokens as actions, allowing for the optimization of models based on complex reward functions like humor. The core of Noose Research's RL infrastructure is then detailed, emphasizing a distributed system with trainer, inference, and environment manager microservices, each designed for scalability and flexibility. For instance, the environment interface is simplified to 'getitem' and 'collect trajectories' functions, enabling multi-turn and multi-agent interactions, and allowing for customized group definitions and token-level manipulations. Emerging industry patterns reflected in this infrastructure prioritize extensibility, allowing for diverse attention masking schemes and custom trainer interactions, ultimately aiming to scale open-source environment development to millions of environments.