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Continuous diffusion language models (dLLMs) are a class of models that apply diffusion models to text generation, generating text in a continuous latent space through a denoising process, and their latent-space-to-text mapping is learned rather than random.
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10/23/2026
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UnverifiedContinuous Diffusion Language Models (CDLM) map text into a continuous embedding space to perform the diffusion process and then decode back to discrete text, rather than performing diffusion directly on discrete tokens.79% similarUnverifiedDiffusion language models (DLM) generate entire passages of text through bidirectional attention and iterative denoising, and control information can be introduced from any position in the sequence.78% similarUnverifiedThe paper "Diffusion-LM Improves Controllable Text Generation" transfers diffusion models to discrete text for controllable generation and is widely recognized as the seminal work in the direction of diffusion language models.73% similarUnverified扩散语言模型（dLLM）支持双向注意力和任意顺序生成，不必像GPT那样从左到右逐个输出token71% similarUnverifiedvLLM is designed specifically for large language models and improves LLM throughput and GPU memory utilization through technologies such as PagedAttention.69% similar
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