@rohanpaul_ai: You can now type what you want in plain English and have a drug-design model chase it. Normally, to make a model genera…

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drug-discovery protein-design large-language-models dpo reinforcement-learning natural-language-control

Summary

Riccardo De Santi et al. introduce LLM-to-Flow Distillation (LFD), a method that lets an LLM grade a scientific generator's outputs against plain-English goals, removing the need for task-specific reward models and outperforming direct reward-function training in drug/molecule design.

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.
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Cached at: 10/16/26, 04:20 AM

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.

Riccardo De Santi (@r_de_santi): 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

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