LFD distills LLM knowledge to steer scientific generators via natural-language goals
hbouammar · x · 2026-10-21
Researchers introduce LLM-to-Flow Distillation (LFD), a method to steer scientific generators (e.g., generative flow models for geometric molecular design) with high-level natural-language discovery goals instead of hand-designed reward functions.
How it works:
- Starting from textual goals like "design peptides with promising anticancer activity" or "design small molecules binding to the D2 dopamine receptor," LFD distills goal-related knowledge from LLMs into generators via iterative DPO on LLM-generated sub-goals
- No task-specific proxy rewards needed; works with any frontier LLM via API access
- Outperforms SOTA reward-guided methods such as FlowGRPO and DiffusionNFT on held-out evaluations
The authors position LFD as bringing natural-language control to any scientific generator, with multiple scientific case studies.
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