$μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915303230799872 |
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| author | Koike-Akino, Toshiaki Liu, Jing Wang, Ye |
| author_facet | Koike-Akino, Toshiaki Liu, Jing Wang, Ye |
| contents | To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these rely on calibration data, domain shift may arise for unknown downstream tasks. With a computationally efficient calibration, activation-aware pruning can be executed for every prompt adaptively, yet achieving reduced complexity at inference. We formulate it as a mixture of micro-experts, called $μ$-MoE. Several experiments demonstrate that $μ$-MoE can dynamically adapt to task/prompt-dependent structured sparsity on the fly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18451 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts Koike-Akino, Toshiaki Liu, Jing Wang, Ye Machine Learning Artificial Intelligence Computation and Language To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these rely on calibration data, domain shift may arise for unknown downstream tasks. With a computationally efficient calibration, activation-aware pruning can be executed for every prompt adaptively, yet achieving reduced complexity at inference. We formulate it as a mixture of micro-experts, called $μ$-MoE. Several experiments demonstrate that $μ$-MoE can dynamically adapt to task/prompt-dependent structured sparsity on the fly. |
| title | $μ$-MoE: Test-Time Pruning as Micro-Grained Mixture-of-Experts |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.18451 |