Frequency Switching Mechanism for Parameter-Efficient Multi-Task Learning

Abstract

Multi-task learning (MTL) aims to equip a single model with the ability to solve multiple tasks efficiently; however, current parameter-efficient fine-tuning (PEFT) methods remain largely limited to single-task adaptation. We introduce Free Sinewich, a parameter-efficient multi-task learning framework that achieves efficient weight reuse through frequency switching. A lightweight Clock Net first determines task dependent frequency with negligible overhead (Free). These frequencies modulate a Sine-AWB (Sinewich) layer, where low-rank factors and convolutional priors are combined into a single kernel and transformed via an elementwise sinusoidal transformation to produce task-specialized weights. Theoretically, sine modulation enhances the rank of low rank adapters, while frequency separation decorrelates the weights of different tasks. On dense prediction benchmarks, Free Sinewich achieves state-of-the-art performance efficiency trade-offs (e.g., up to +5.39% improvement over single-task fine-tuning with only 6.53M trainable parameters), offering a compact and scalable paradigm based on frequency-based parameter sharing. Our code is publicly available.