LFD Distills Natural Language Goals from LLMs into Molecular Generators, Beating FlowGRPO
AllThingsApx · x · 2026-10-15
Researchers introduce LLM-to-Flow Distillation (LFD, arXiv:2610.17246), a method that aligns scientific flow/diffusion generators with high-level natural-language discovery goals.
- A frozen LLM acts as an adaptive teacher, building a curriculum of sub-goals (e.g. "design peptides with promising anticancer activity") matched to the flow's current capability
- Candidate rankings from the LLM drive iterative DPO fine-tuning — no task-specific proxy rewards needed, works with any frontier LLM via API
- Outperforms reward-guided SOTA methods like FlowGRPO and DiffusionNFT on held-out tasks
Team includes Andreas Krause and Michael Bronstein; paper and code are public.
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