Block Parallelism For Efficient Distributed Long-Context Diffusion Language Model Training

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parallelismmodel trainingdistributed systemslanguage models

2609.19242

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80%

2h ago

Abstract

This paper introduces block parallelism (BP) and context-sharded block parallelism (CSBP) to improve the efficiency of training block diffusion language models (BDLMs) in distributed settings.

Reality Card

Core Claim

CSBP improves throughput by 1.18-1.45x for supervised fine-tuning and 1.27-1.33x for converting autoregressive models to BDLMs on 16 H200 GPUs at 256K context.

Method / Result

CSBP accelerates DFlash2 speculative-decoder training by 2.48x at 512K and 7.59x at 1M on eight H100 GPUs.

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