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S35 | SOL: Measuring Gaps between Text Distributions
Gregor Kornhardt presents SOL, a distance between text distributions for evaluating diffusion and flow-based language models, which only provide likelihood bounds of varying tightness. SOL represents each sequence by the empirical measure of its hidden states under a fixed transformer and compares the distributions of these measures with the double sliced Wasserstein distance, which the authors prove is a metric when the transformer is injective. SOL detects distributional failures, recovers expected model trends, and gives stable sample-based estimates.
S34 | Simplex Diffusion Models
Justin Deschenaux presents Simplex Diffusion Models, which lift the diffusion process to the probability simplex so that beliefs over categories carry across denoising steps instead of collapsing through categorical sampling. SDMs have closed-form reverse transitions, train with a simple cross-entropy loss, and use a DDIM-like sampler with tunable stochasticity. They are competitive with strong discrete diffusion baselines on OpenWebText, beat masked and uniform diffusion on code generation, and, distilled to 8 steps, solve more GSM8K problems than distilled discrete diffusion models using 128 steps.
S33 | Distilling Autoregressive Models into One-step Flow Maps
Yeongmin Kim presents Gumbel Straight Flow, a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive model. The authors show theoretically that the coupling between Gumbel noise and one-hot token sequences induced by an autoregressive model yields non-intersecting linear paths connecting the noise to the sequence representations, which is what makes few-step sampling tractable. GSF is trained with a flow map semigroup objective whose tangent condition is guided directly by the AR teacher, and it outperforms current few-step language generation baselines across pretraining and downstream benchmarks.
Featured Videos
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How did diffusion LLMs get so fast?
Techniques for accelerating diffusion LLMs, from self-distillation and curriculum learning to KV caching and block diffusion
But How Do Diffusion Language Models Actually Work?
Jia-Bin Huang explores several ideas for applying diffusion models to language modeling
Simple Diffusion Language Models
Quick introduction to Masked Diffusion Language Models (MDLM) by Alexander Rush
About the Reading Group
Diffusion LLMs are faster, more controllable successors to traditional LLMs and are rapidly gaining adoption. This reading group builds a community for exchanging and debating emerging ideas in this space. While our primary focus is discrete diffusion models for language, we also welcome work on other modalities and applications, such as molecular design, drug discovery, and beyond.
Meet the Organizers
Subham Sahoo
Holds a Ph.D. from Cornell Tech, where he specialized in Diffusion Language Models. He has made foundational contributions to the field, with his work deployed at scale by Google, NVIDIA, and ByteDance across language generation and drug discovery.
Justin Deschenaux
PhD student in Machine Learning at EPFL, advised by Prof. Caglar Gulcehre. Previously interned at Apple MLR. His research interests include diffusion language models, fast generative models, and generalization.
