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Avengers assemble: 1 year BakerLab + NVIDIA GenAIR = 🔥 rfd4-proteina.org
Presenting RFD4-Proteina, our latest protein design model, significantly better and faster than everything we compared against, both in silico and in the lab! 1/30View quoted post on X
Today, we introduce Priming Models, a new paradigm for training one-step generative models from scratch.
No diffusion teacher. No flow map. No CFG. No JVP.
We believe this work will challenge how we think about building one-step models.
Joint work with @sitanch, @du_yilun, @parkwbin03, Bohyung Han.
🔥 1.19 FID one-step on ImageNet 256×256 while using substantially less compute, e.g., 12X less than Drifting-B, 6X less than Drifting-L
🎨 Scales to text-to-image generation (GenEval 0.82)
🤖 World modeling on RT-1 / Bridge-V2
Priming Model is built on one core message: [Reconstruction pretraining --> Distributional post-training].View quoted post on X
How do continuous and discrete diffusion LMs compare on reasoning, and what might explain the differences? 🤔
We systematically explore this with theory & experiments, and find FLMs ahead in few-step structured reasoning.
Theory shows how temporal superposition lets FLMs keep alternative answers for later reasoning.
📄 More details 🧵⬇️View quoted post on X
🔥 Accelerate your diffusion LMs with Distribution Matching Distillation
Introducing two methods for continuous diffusion LMs with gains at complementary sampling budgets:
🔺 Simplex-DMD at very few-step budgets
🎲 Reinforce-DMD at larger budgets
🔺 At 4 NFEs Simplex-DMD halves the strongest baseline’s generative perplexity at matched entropy
(1/n)View quoted post on X
