dLLM: Simple Diffusion Language Modeling

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• The dLLM framework unifies core components of diffusion language modeling including training, inference, and evaluation.method 95%
• dLLM enables users to reproduce, finetune, deploy, and evaluate open-source large DLMs such as LLaDA and Dream through a standardized pipeline.technical 90%
• The framework provides minimal, reproducible recipes for building small DLMs from scratch with accessible compute.method 90%
• dLLM can convert any BERT-style encoder or autoregressive LM into a diffusion language model.technical 85%
• Current diffusion language model components are distributed across ad-hoc research codebases or lack transparent implementations.limitation 90%
• The field of diffusion language modeling is evolving quickly and accelerating.market 85%
• The authors release checkpoints of small DLMs to make DLMs more accessible and accelerate future research.result 90%
• There is a clear need for a unified framework that standardizes common DLM components while remaining flexible enough to support new methods.market 80%

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