We propose Interactive Retrosynthesis Planning, a novel framework that learns to construct retrosynthetic routes by interacting with tree MDPs and optimising a worst-path objective by self-imitation learning.

An LLM-based retrosynthesis planning agent trained end-to-end with Agentic Reinforcement Learning.