Unlocking the Black Box: The Stepwise Informativeness Assumption Explains How LLMs Reason

This fascinating research brilliantly bridges the gap between empirical observation and theoretical understanding in Generative AI. By introducing the Stepwise Informativeness Assumption (SIA), the researchers provide a groundbreaking mathematical framework explaining exactly why internal entropy dynamics correlate with correct answers. It is incredibly exciting to see how standard fine-tuning and Reinforcement Learning pipelines naturally encourage models to accumulate vital reasoning clues step-by-step!