/AI22d ago

Entropy-gated bitstream diffusion matches autoregressive model performance

Researchers introduce entropy-gated bitstream diffusion, a continuous language modeling technique that operates directly on bitstreams using entropy profiles to focus training. The method outperforms masked and uniform diffusion baselines in evaluations and reaches performance comparable to autoregressive language models under the same settings. A related ICML paper adapts existing autoregressive models to diffusion frameworks through implicit representation alignment.

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#1205

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Luca Ambrogioni#1824

Gabriel Raya@gaboraya

At the core of efficient diffusion is a simple question: where is information actually resolved?

The entropy profile answers this, guiding training effort toward the regions where structure is formed. Great to see this perspective used for continuous bitstream language diffusion

Luca Ambrogioni@LucaAmb

1/?) As promised to Sander Dieleman (@sedielem), we’re finally excited to share:

Towards Closing the Autoregressive Gap in Language Modeling via Entropy-Gated Continuous Bitstream Diffusion

We show that continuous diffusion can achieve very strong language modeling performance when operating directly on bitstreams, outperforming masked and uniform diffusion baselines, and essentially matching autoregressive models under our evaluation settings.

1:46 AM · May 16, 2026 · 1.1K Views

/AI22d ago

Entropy-gated bitstream diffusion matches autoregressive model performance

--0--

#1205

Original post

Luca Ambrogioni#1824

Gabriel Raya@gaboraya

At the core of efficient diffusion is a simple question: where is information actually resolved?

The entropy profile answers this, guiding training effort toward the regions where structure is formed. Great to see this perspective used for continuous bitstream language diffusion

Luca Ambrogioni@LucaAmb

1/?) As promised to Sander Dieleman (@sedielem), we’re finally excited to share:

Towards Closing the Autoregressive Gap in Language Modeling via Entropy-Gated Continuous Bitstream Diffusion

1:46 AM · May 16, 2026 · 1.1K Views

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Positive users thank the authors and add the paper adapting autoregressive LMs to diffusion models via representation alignment to their reading lists due to its clear technical value.

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Fred Peng@pengzhangzhi1

@zhuci19 thanks!! added to my reading list : )

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Cai Zhou@zhuci19

Nice work! Our ICML paper utilizes another implicit representation alignment strategy: generating discrete tokens and continuous representations at the same time, analogously to Latent Forcing or ReDi - see Section 4.2 of our paper for more details https://arxiv.org/abs/2510.03206 This leads to a 25x acceleration compared with pure discrete baselines.

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