Ariadne：跨异构 Transformer 免训练迁移微调行为
From Parameter Rebasin to Functional Transfer Across Heterogeneous Transformers
一篇 arXiv 论文提出 Ariadne，不用重新训练就能把微调效果迁移到不同大小、不同结构的 Transformer 上，比 rebasin 方法更快更省内存。
arXiv 论文提出 Ariadne，一种 training-free 方法，把任务向量在参数空间的搬运改为刻画微调在匹配块上引起的激活变化。方法先用局部 Procrustes 映射对齐到目标基座，再用闭式岭回归直接合成目标模型的原生更新。在 Transformer 扩展、缩减和语言模型迁移三类场景中，Ariadne 提升了目标模型性能，同时相比跨架构 rebasin 方法显著降低构建更新所需的时间和内存。
From Parameter Rebasin to Functional Transfer Across Heterogeneous Transformers
Adapting pretrained Transformers to downstream tasks produces task-specific updates that are expensive to reproduce for every model variant. Reusing these updates across different depths and widths is challenging because their parameters have incompatible shapes and do not share a canonical coordinate system. We introduce Ariadne, a training-free method that transfers task-specific behavior across heterogeneous Transformers without resizing the source model or transporting its parameters. Rather than treating the source task vector, i.e. the difference in parameter space between the fine-tuned and the base models, as the object to move, Ariadne characterizes fine-tuning by the activation changes it induces at matched blocks. First, it aligns these effects to a target base through local Procrustes maps. Then, it directly synthesizes a native target update by closed-form ridge regression. Across Transformer expansion, reduction and language-model transfer, Ariadne improves target-model performance while substantially reducing the time and memory required to construct an update relative to previous cross-architecture rebasin approaches. These results show that local functional effects provide an efficient alternative to parameter transport.