Drop-Upcycling: Training Sparse Mixture of Experts with Partial
  Re-initialization
The Mixture of Experts (MoE) architecture reduces the training and inference
cost significantly compared to a dense model of equivalent capacity. Upcycling
is an approach that initializes and trains an MoE model using a pre-trained
dense model. While upcycling leads to initial performance gains, the training
progresses slower than when trained from scratch, leading to suboptimal
performance in the long term. We propose Drop-Upcycling - a method that
effectively addresses this problem. Drop-Upcycling combines two seemingly
contradictory approaches: utilizing the knowledge of pre-trained dense models
while statistically re-initializing some parts of the weights. This approach
strategically promotes expert specialization, significantly enhancing the MoE
model's efficiency in knowledge acquisition. Extensive large-scale experiments
demonstrate that Drop-Upcycling significantly outperforms previous MoE
construction methods in the long term, specifically when training on hundreds
of billions of tokens or more. As a result, our MoE model with 5.9B active
parameters achieves comparable performance to a 13B dense model in the same
model family, while requiring approximately 1/4 of the training FLOPs. All
experimental resources, including source code, training data, model checkpoints
and logs, are publicly available to promote reproducibility and future research
on MoE.
67c07170af68756abc571ab8
Taishi Nakamura
claimed_verified
2025-02-27T22:09:06.783Z
6308c49c454dc257521bc7f9
https://cdn-avatars.huggingface.co/v1/production/uploads/6308c49c454dc257521bc7f9/UWUS6OPa6OpVu1T0gd-wJ.jpeg
Taishi
user
Taishi-N324
67c07170af68756abc571ab9
Takuya Akiba
claimed_verified
2025-02-28T12:15:52.711Z
6482810dba6c556892f6f257
https://cdn-avatars.huggingface.co/v1/production/uploads/6482810dba6c556892f6f257/c7-wiVKenXiRtwnRpnjZN.jpeg
Takuya Akiba
user
iwiwi
67c07170af68756abc571aba
Kazuki Fujii
67c07170af68756abc571abb
Yusuke Oda
67c07170af68756abc571abc
Rio Yokota
67c07170af68756abc571abd
Jun Suzuki
Drop-Upcycling: Training Sparse Mixture of Experts with Partial
  Re-initialization
The Mixture of Experts (MoE) architecture reduces the training and inference
cost significantly compared to a dense model of equivalent capacity. Upcycling
is an approach that initializes and trains an MoE model using a pre-trained
dense model. While upcycling leads to initial performance gains, the training
progresses slower than when trained from scratch, leading to suboptimal
performance in the long term. We propose Drop-Upcycling - a method that
effectively addresses this problem. Drop-Upcycling combines two seemingly
contradictory approaches: utilizing the knowledge of pre-trained dense models
while statistically re-initializing some parts of the weights. This approach
strategically promotes expert specialization, significantly enhancing the MoE
model's efficiency in knowledge acquisition. Extensive large-scale experiments
demonstrate that Drop-Upcycling significantly outperforms previous MoE
construction methods in the long term, specifically when training on hundreds
of billions of tokens or more. As a result, our MoE model with 5.9B active
parameters achieves comparable performance to a 13B dense model in the same
model family, while requiring approximately 1/4 of the training FLOPs. All
experimental resources, including source code, training data, model checkpoints
and logs, are publicly available to promote reproducibility and future research
on MoE.
Drop-Upcycling enhances MoE models by combining pre-trained knowledge with selective re-initialization, improving long-term performance and efficiency.
Mixture of Experts MoE upcycling Drop-Upcycling expert specialization training FLOPs