aiqbench analysis
Mixture-of-Control (MoC) advances state-based fine-tuning by enabling efficient cross-layer communication between transformer blocks, addressing the limitations of per-block updates that restrict representational adaptation. By treating block-wise control states as experts in a sparse mixture-of-experts process, this method improves performance across 33 datasets while maintaining low memory and compute overhead compared to weight-based alternatives.
Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models
arXiv:2606.31397v2 Announce Type: replace Abstract: State-based fine-tuning has emerged as a compelling alternative to weight-based adaptation for transformers, updating lightweight controls into states rather than model weights, offering substantial memory savings while retaining parameter efficiency. However, most existing state-based methods typically apply only per-block control updates, which limits inter-block information exchange and restricts representational adaptation. Meanwhile, prior mechanisms that enable cross-block communication often introduce considerable computational overhead, reducing their practicality for efficient fine-tuning. We introduce Mixture-of-Control (MoC), an efficient cross-layer communication that adaptively integrates local and global control signals to enhance representation learning during adaptation. MoC treats block-wise control states as experts in a sparse mixture-of-experts process, enabling efficient communication across transformer blocks. Experiments on 33 datasets and 10 pretrained models across diverse transformer-based benchmarks demonstrate that MoC consistently outperforms existing state-based fine-tuning methods while maintaining comparable efficiency in memory and compute.
Abstract:State-based fine-tuning has emerged as a compelling alternative to weight-based adaptation for transformers, updating lightweight controls into states rather than model weights, offering substantial memory savings while retaining parameter efficiency. However, most existing state-based methods typically apply only per-block control updates, which limits inter-block information exchange and restricts representational adaptation. Meanwhile, prior mechanisms that enable cross-block communication often introduce considerable computational overhead, reducing their practicality for efficient fine-tuning. We introduce Mixture-of-Control (MoC), an efficient cross-layer communication that adaptively integrates local and global control signals to enhance representation learning during adaptation. MoC treats block-wise control states as experts in a sparse mixture-of-experts process, enabling efficient communication across transformer blocks. Experiments on 33 datasets and 10 pretrained models across diverse transformer-based benchmarks demonstrate that MoC consistently outperforms existing state-based fine-tuning methods while maintaining comparable efficiency in memory and compute.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.31397 [cs.LG]
(or arXiv:2606.31397v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.31397 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 232 words