title: Router-Tuning: A Simple and Effective Approach for Enabling
    Dynamic-Depth in Transformers
summary: Traditional transformer models often allocate a fixed amount of computational
  resources to every input token, leading to inefficient and unnecessary
  computation. To address this, the Mixture of Depths (MoD) was introduced to
  dynamically adjust the computational depth by skipping less important layers.
  Despite its promise, current MoD approaches remain under-explored and face two
  main challenges: (1) high training costs due to the need to train the
  entire model along with the routers that determine which layers to skip, and
  (2) the risk of performance degradation when important layers are
  bypassed. In response to the first issue, we propose Router-Tuning, a method
  that fine-tunes only the router on a small dataset, drastically reducing the
  computational overhead associated with full model training. For the second
  challenge, we propose MindSkip, which deploys Attention with Dynamic
  Depths. This method preserves the model's performance while significantly
  enhancing computational and memory efficiency. Extensive experiments
  demonstrate that our approach delivers competitive results while dramatically
  improving the computation efficiency, e.g., 21\% speedup and only a 0.2\%
  performance drop. The code is released at
  https://github.com/CASE-Lab-UMD/Router-Tuning.
paper_authors._id: 6717ac546136223abbfd80bf
paper_authors.name: Shwai He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T13:48:00.289Z
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paper_authors.avatarUrl: /avatars/ff05994acd7da64a2ed6e44f12cf77bc.svg
paper_authors.fullname: Shwai He
paper_authors.type: user
paper_authors.user: Shwai
paper_authors._id: 6717ac546136223abbfd80c0
paper_authors.name: Tao Ge
paper_authors._id: 6717ac546136223abbfd80c1
paper_authors.name: Guoheng Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T13:48:19.181Z
paper_authors._id: 6438f5555aa69077ffb16218
paper_authors.avatarUrl: /avatars/582f736c0485252010b682e85db62e77.svg
paper_authors.fullname: Guoheng Sun
paper_authors.type: user
paper_authors.user: s1ghhh
paper_authors._id: 6717ac546136223abbfd80c2
paper_authors.name: Bowei Tian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-22T13:48:27.178Z
paper_authors._id: 6623e96dd7b6076f66480898
paper_authors.avatarUrl: /avatars/62288faa8ecc69c6b1cef4bc553941b9.svg
paper_authors.fullname: Bowei Tian
paper_authors.type: user
paper_authors.user: abdd68
paper_authors._id: 6717ac546136223abbfd80c3
paper_authors.name: Xiaoyang Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-30T09:55:19.525Z
paper_authors._id: 657cd228138b7e391444a65d
paper_authors.avatarUrl: /avatars/c7c984ae483144fab627aa2c54d91d0f.svg
paper_authors.fullname: Xiaoyang Wang
paper_authors.type: user
paper_authors.user: xywang1
paper_authors._id: 6717ac546136223abbfd80c4
paper_authors.name: Ang Li
paper_authors._id: 6717ac546136223abbfd80c5
paper_authors.name: Dong Yu
paper_title: Router-Tuning: A Simple and Effective Approach for Enabling
    Dynamic-Depth in Transformers
paper_summary: Traditional transformer models often allocate a fixed amount of computational
  resources to every input token, leading to inefficient and unnecessary
  computation. To address this, the Mixture of Depths (MoD) was introduced to
  dynamically adjust the computational depth by skipping less important layers.
  Despite its promise, current MoD approaches remain under-explored and face two
  main challenges: (1) high training costs due to the need to train the
  entire model along with the routers that determine which layers to skip, and
  (2) the risk of performance degradation when important layers are
  bypassed. In response to the first issue, we propose Router-Tuning, a method
  that fine-tunes only the router on a small dataset, drastically reducing the
  computational overhead associated with full model training. For the second
  challenge, we propose MindSkip, which deploys Attention with Dynamic
  Depths. This method preserves the model's performance while significantly
  enhancing computational and memory efficiency. Extensive experiments
  demonstrate that our approach delivers competitive results while dramatically
  improving the computation efficiency, e.g., 21\% speedup and only a 0.2\%
  performance drop. The code is released at
  https://github.com/CASE-Lab-UMD/Router-Tuning.
paper_ai_keywords: Mixture of Depths (MoD) Router-Tuning MindSkip Attention with Dynamic Depths