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BlockGen model explores blockwise sequence generation with hybrid samplers

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced BlockGen, a novel blockwise sequence modeling approach that utilizes hybrid samplers for discrete diffusion. This method explores the effectiveness of uniform-state diffusion models (USDMs) compared to masked diffusion models (MDMs) when generating sequences in blocks rather than token by token. BlockGen integrates autoregressive (AR) predictions with diffusion models to refine unlikely tokens, demonstrating competitive performance on tasks like GSM8K and OpenWebText. AI

IMPACT Introduces a new method for blockwise sequence generation, potentially improving efficiency and performance in discrete diffusion models.

RANK_REASON The cluster contains an academic paper detailing a new modeling approach.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Justin Deschenaux, Caglar Gulcehre ·

    BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers

    arXiv:2606.02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion? Recent studies indicate that this may be the case. In combination with predictor-corrector samplers, uniform-state diffusion models (USDMs) pr…

  2. arXiv cs.LG TIER_1 English(EN) · Caglar Gulcehre ·

    BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers

    Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion? Recent studies indicate that this may be the case. In combination with predictor-corrector samplers, uniform-state diffusion models (USDMs) produce samples of higher-quality than masked diff…

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