LangFlow is the first continuous diffusion language model to rival discrete diffusion on perplexity and generative perplexity while exceeding autoregressive baselines on several zero-shot tasks.
α-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models.arXiv:2504.10283
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A geometric latent-subspace model on Riemannian manifolds of categorical distributions enables low-dimensional generative modeling of discrete data via isometries and geometric PCA for flow matching.
TR-CIE sampler improves DFM sampling quality under limited NFE via schedule-based time reparameterization and cumulative-intensity extrapolation without extra model calls.
Mixture-of-experts flow matching enables non-autoregressive language models to achieve autoregressive-level quality in three sampling steps, delivering up to 1000x faster inference than diffusion models.
citing papers explorer
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LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling
LangFlow is the first continuous diffusion language model to rival discrete diffusion on perplexity and generative perplexity while exceeding autoregressive baselines on several zero-shot tasks.
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Generative Modeling of Discrete Data Using Geometric Latent Subspaces
A geometric latent-subspace model on Riemannian manifolds of categorical distributions enables low-dimensional generative modeling of discrete data via isometries and geometric PCA for flow matching.
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A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
TR-CIE sampler improves DFM sampling quality under limited NFE via schedule-based time reparameterization and cumulative-intensity extrapolation without extra model calls.
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Towards Faster Language Model Inference Using Mixture-of-Experts Flow Matching
Mixture-of-experts flow matching enables non-autoregressive language models to achieve autoregressive-level quality in three sampling steps, delivering up to 1000x faster inference than diffusion models.