"LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators" Scientific flow models can generate valid molecules and peptides, but struggle to follow high-level natural-language goals. On top of that, current methods also rely on explicit reward functions, which are unreliable in scientific discovery. LLM-to-Flow Distillation (LFD) instead uses an LLM to create progressively harder sub-goals based on what the generator can currently produce, then uses its preferences…