互补自蒸馏如何维护大模型上下文完整性？这项研究提出双模型协作新方案，为LLM安全对齐提供创新思路。
arXiv:2605.20258v1 Announce Type: new Abstract: Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing i…
最新研究：后训练MoE模型通过自蒸馏跳过一半专家，无需从头预训练，显著降低计算量。
arXiv:2605.18643v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) scales language models efficiently through sparse expert activation, and its …
扩散LLM无需外部教师，通过“展开回退”策略自我提升推理效率，开辟模型加速新方向。
arXiv:2605.16941v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still …
多模态大模型新突破，通过自蒸馏策略让AI学会捕捉视觉细节，显著提升细粒度理解能力。
arXiv:2605.18740v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, wher…
从长时域智能体学习挑战入手，提出目标后见自蒸馏方法，提升复杂任务表现。
arXiv:2605.17873v1 Announce Type: new Abstract: Training long-horizon LLM agents with reinforcement learning is challenging because sparse outcome rew…
提出轨迹自蒸馏方法，让扩散语言模型用少步就能快速并行生成文本，突破推理速度瓶颈。
arXiv:2602.12262v3 Announce Type: replace-cross Abstract: Diffusion large language models (DLLMs) have emerged as powerful generative models with the …
提出粒度自适应优势重加权方法，用自蒸馏实现LLM Agent的细粒度信用分配，改进策略学习效率。
arXiv:2605.11853v2 Announce Type: replace-cross Abstract: Reinforcement learning has become a widely used post-training approach for LLM agents, where…