LLM-to-Flow Distillation：用自然语言目标操控药物设计生成模型
LLM-to-Flow Distillation 用自然语言目标操控药物设计生成模型
LLM-to-Flow Distillation 让用户用自然语言描述目标，由 LLM 直接给药物设计生成模型的输出打分，省去为每个目标训练奖励模型。基础分子模型在 38,500 个样本中产出 0 个可用命中，几轮迭代后每 512 个样本可产出 1 个。该方法在评测中以 69.06 对 57.04 击败直接针对真实评分函数训练的版本，且全程未接触任何真实分数。
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.
来源：Rohan Paul · x.com