RLVR（基于可验证奖励的强化学习）简单而粗暴：别听人的，听结果的。成为LLM的AlphaZero时刻，探索→验证→强化，还引入了全新Scaling Law 测试时计算。 （2）Vibe Coding（氛围编码）的流行：Vibe ...

Diffusion Transformers在生成高质量图像方面展现出强大的能力。然而，随着模型规模的增大，其不断增长的内存占用和推理延迟给实际部署带来了重大挑战。近期在大语言模型（LLMs）领域的研究表明，基于旋转技术能够平滑异常值并实现4比特量化，但这类方法通常会产生显著的额外开销，且难以处理Diffusion Transformers中的行方向异常值。

这项由上海交通大学邓志杰教授团队领导的研究发表于2025年1月，论文题为《Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing》。

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