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To address this, we use multi head randomization in R LoRA, combining random initialization and multi head dropout to diversify starting points and inputs, thereby improving task specific learning.

The project aims to enhance multi task adaptation for Multi Head LoRA via Multi Head randomization. The code is provided to facilitate reproducibility and to allow others to build upon this work.

we propose R LoRA, which incorporates Multi Head Ran domization. Multi Head Randomization diver sifies the head matrices through Multi Head Dropout and Multi Head Random Initialization, enabling more efficient learning of task s. ecific features while maintaining shared knowledge representation. Our approach not only im proves perf.

Essentially, to start from the pretrained model as initialization for finetuning, one can either initializeBto zero and A to random (default initialization in PEFT package), or vice versa.

The warnings indicate that some weights were not used (the ones corresponding to the dropped pretraining head) and that some others were randomly initialized (the ones for the new head). It concludes by encouraging you to train the model, which is exactly what we are going to do now.

TL;DR: We propose a parameter efficient fine tuning method that improves the performance of LoRA in the presence of computational and data heterogeneity in FL.

In real world scenarios, LLMs are fine tuned on data from multiple domains to perform tasks across various fields, embodying multi task learning (MTL). LoRA often underperforms in such complex scenarios.

Popular flow: pretrain, re initialize a random head, then finetune together But Kummar et.al claim hasty finetune will distort learned features. They propose a two phase finetuning: Head probing: only update task head

The system uses a novel initialization strategy where each task head starts with different random values, creating distinct learning trajectories. This prevents the catastrophic forgetting often seen in multi task models while maintaining the efficiency benefits of parameter efficient fine tuning.

Multi Head Randomization diversifies the head matrices through Multi Head Dropout and Multi Head Random Initialization, enabling more efficient learning of task specific features while maintaining shared knowledge representation. Our approach not only improves performance in MTL but also reduces GPU memory usage and training time.

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