LFD 方法：用自然语言目标引导药物设计模型
You can now type what you want in plain English and have a drug-design model chase it.
研究者提出 LLM-to-Flow Distillation（LFD），通过迭代 DPO 将 LLM 生成的高层目标知识蒸馏进科学生成模型，无需任务专属代理奖励，仅凭自然语言目标即可引导分子生成。基础分子模型在 38,500 个样本中产出 0 个可用命中，经过几轮迭代后每 512 个样本可产出 1 个。在留出计算评测中，LLM 打分模型以 69.06 对 57.04 超过直接针对真实评分函数训练的版本。
查看原帖We’re bringing natural-language control to any scientific generator. We are very excited to introduce LLM-to-Flow Distillation (LFD), a method to adapt scientific generators to high-level natural-language discovery goals. Starting from textual goals like “design peptides with promising anticancer activity” or “design small molecules binding to the D2…
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We’re bringing natural-language control to any scientific generator. We are very excited to introduce LLM-to-Flow Distillation (LFD), a method to adapt scientific generators to high-level natural-language discovery goals. Starting from textual goals like “design peptides with promising anticancer activity” or “design small molecules binding to the D2 dopamine receptor”, LFD distills high-level goal-related knowledge from LLMs into scientific generators through iterative DPO on LLM-generated sub-goals. 🔥 LFD does not need task-specific proxy rewards, works with any frontier LLM via API-access, and outperforms state-of-the-art reward-guided methods such as FlowGRPO and DiffusionNFT on held-out computational evaluations. (1/6)
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You can now type what you want in plain English and have a drug-design model chase it.
Normally, to make a model generate better drug candidates, you need a reward model trained on that exact target. Here they deleted that step and let an LLM grade the outputs instead, just by reading your goal in English.
Any lab that couldn’t afford to build a scoring model for its target now has a path to a steered generator. That was the gate keeping small teams out of generative design, and it just moved.
RLHF has the same cold-start problem everywhere: if the base model never produces a good output, there’s nothing to rank. So the LLM writes an easier goal first, grades against that, and keeps raising the bar as the model gets better.
The base molecule model produced 0 usable hits in 38,500 samples. After a few rounds of this, it produced one in a batch of 512.
The LLM’s scores are flat-out wrong a lot of the time, and it doesn’t matter, because DPO only needs “A is better than B”, not an accurate number.
The LLM-graded model beat the versions that were allowed to train directly against the real scoring function, 69.06 vs 57.04, while never seeing a single score.