Principled RL for Diffusion LLMs Emerges from a Sequence-Level Perspective

The article likely discusses a novel approach to Reinforcement Learning (RL) applied to Large Language Models (LLMs) that utilize diffusion models. The focus is on a sequence-level perspective, suggesting a method that considers the entire sequence of generated text rather than individual tokens. This could lead to more coherent and contextually relevant outputs from the LLM.