Reinforcement Learning Fuels Advancements in Large Language Models

Reinforcement learning, fueled by a rapidly expanding open-source ecosystem with diverse libraries, is driving advancements in large language models, enabling optimization for properties like adoption, components like trainers and generators, and use cases like Reinforcement Learning from Human Feedback and multi-turn agentic RL.

• Reinforcement learning has become integral to modern large language model development
• Open-source ecosystem for RL libraries is rapidly growing with diverse design principles and optimization choices
• Libraries are evaluated on adoption, system properties, components like trainer and generator, and support for different use cases like RLHF and multi-turn agentic RL