Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
The episode explores 'Round-trip Reinforcement Learning,' a method for training Chemical LLMs to self-check their work. It addresses traditional RL's lack of long-term physical consistency by implementing an iterative, quantifiable constraint. Improvements include integrating external knowledge bases and making the self-correction process adaptive, transforming AI into a verifiable discovery partner.