Structure-Preserving Reinforcement Learning for Language Models: Leveraging Geometry
We propose a geometric framework for structure-preserving reinforcement learning in the fine-tuning of large language models (LLMs), grounded in Lie algebraic geometry. The approach systematically identifies geometric constraints in representation spaces by analyzing semantic transformations and detecting cases where e...