（来源：机器之心）自回归（AR）大语言模型逐 token 顺序解码的范式限制了推理效率；扩散 LLM（dLLM）以并行生成见长，但过去难以稳定跑赢自回归（AR）模型，尤其是在 KV Cache 复用、和 可变长度 支持上仍存挑战。Fast-dLLMv2给出了一条务实路线：将预训练 AR 模型适配为适配为能并行解码的 Block-dLLM—— 且只需～1B tokens 量级的微调即可达到 “无损”...
Source Link（来源：机器之心）自回归（AR）大语言模型逐 token 顺序解码的范式限制了推理效率；扩散 LLM（dLLM）以并行生成见长，但过去难以稳定跑赢自回归（AR）模型，尤其是在 KV Cache 复用、和 可变长度 支持上仍存挑战。Fast-dLLMv2给出了一条务实路线：将预训练 AR 模型适配为适配为能并行解码的 Block-dLLM—— 且只需～1B tokens 量级的微调即可达到 “无损”...
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