A Theoretical Lens for RL-Tuned Language Models via Energy-Based Models

This article likely explores the theoretical underpinnings of Reinforcement Learning (RL) tuned Language Models (LLMs) using Energy-Based Models (EBMs). The focus is on providing a theoretical framework for understanding and potentially improving the behavior of LLMs trained with RL. The use of EBMs suggests an approach that models the probability distribution of the LLM's outputs based on an energy function, allowing for a different perspective on the learning process compared to standard RL methods. The source being ArXiv indicates this is a research paper, likely detailing novel theoretical contributions.