扩散变换器虽强但计算开销大，后训练剪枝技术如何高效优化？这篇论文给出新方案。
arXiv:2607.00927v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have demonstrated impressive performance in image generation but suffe…
提出拓扑感知层剪枝方法，有效降低大视觉语言模型的计算与内存成本，助力高效部署。
arXiv:2604.16502v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understandi…
突破多模态LLM训练中计算与内存的帕累托前沿，BigMac提出高效优化方案。
arXiv:2605.25451v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. …
用Wald的序贯概率比检验动态调控LLM辩论轮数，显著提升推理效率与资源利用率
arXiv:2605.19193v1 Announce Type: new Abstract: Multi-agent LLM debate improves factuality and reasoning, but most recipes pick a fixed round count, o…
提出Learning-Zone Energy方法，在线选择数据以提升RL后训练效率，避免均匀分配浪费计算。
arXiv:2605.17003v1 Announce Type: new Abstract: Reinforcement Learning (RL) post-training has emerged as the dominant paradigm for eliciting mathemati…
极端压缩链式思考，大幅提升大模型推理效率，减少计算开销，创新方法值得关注。
arXiv:2602.08324v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Languag…