LLM-to-Flow Distillation Adapts Scientific Generators to Natural-Language Discovery Goals
rbhar90 · x · 2026-10-15
Researchers introduce LLM-to-Flow Distillation (LFD), a method to steer scientific generators with high-level natural-language goals (e.g., designing anticancer peptides or D2 receptor-binding molecules).
- Distills goal knowledge from LLMs via iterative DPO on LLM-generated sub-goals
- Needs no task-specific proxy rewards; works with any frontier LLM via API
- Outperforms reward-guided SOTA like FlowGRPO and DiffusionNFT on held-out evaluations
A new AI-for-Science training method relevant to molecule/protein generation.
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