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ARXIV:2602.16813 · LANGUAGE MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.16813LANGUAGE MODELINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality.
Opportunity summary
Pain Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to…
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Here we show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed.
Language Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality.
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10.48550/arXiv.2602.16813Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality.
Abstract
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this promise. Here we show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed. By revisiting the fundamentals of flows over discrete modalities, we build a flow-based language model (FLM) that performs Euclidean denoising over one-hot token encodings. We show that the model can be trained by predicting the clean data via a cross entropy objective, where we introduce a simple time reparameterization that greatly improves training stability and generation quality. By distilling FLM into its associated flow map, we obtain a distilled flow map language model (FMLM) capable of few-step generation. On the LM1B and OWT language datasets, FLM attains generation quality matching state-of-the-art discrete diffusion models. With FMLM, our approach outperforms recent few-step language models across the board, with one-step generation exceeding their 8-step quality. Our work calls into question the widely held hypothesis that discrete diffusion processes are necessary for generative modeling over discrete modalities, and paves the way toward accelerated flow-based language modeling at scale. Code is available at https://github.com/david3684/flm.
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PROBLEM
Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this promise.
METHOD
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this pr...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Here we show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed.
WHY NOW
Language Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this promise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. In practice, however, they exhibit a sharp degradation of sample quality in the few-step regime, failing to realize this promise.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Here we show that language models leveraging flow-based continuous denoising can outperform discrete diffusion in both quality and speed.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Language Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Accelerate language model generation with a flow-based denoising approach outperforming discrete diffusion in both speed and quality.
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