How Mixture-of-Adaptations Makes Language Model Fine-Tuning Cheaper and Smarter

Mixture-of-Adaptations (MoA) introduces stochastic routing, consistency regularization, and module merging to make large language model fine-tuning more parameter-efficient. By randomly routing inputs across adaptation modules, then merging or averaging their weights, MoA reduces FLOPs and computational cost without sacrificing performance. This approach connects with Bayesian inference and model ensembling, offering a robust yet efficient path to adapting LLMs.