Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models

arXiv:2606.31397v1 Announce Type: new 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, r

Source: arXiv cs.LG — read the full report at the original publisher.

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