RandLoRA Enables Efficient Full Rank Fine Tuning of Large Language Models

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Efficient Fine-tuning of Large Language Models: RandLoRA Enables Full-Rank Updates

Fine-tuning large language models (LLMs) is essential for adapting them to specific tasks. However, traditional fine-tuning, where all model parameters are adjusted, is resource-intensive and requires significant storage space. Parameter-efficient methods like Low-Rank Adaptation (LoRA) have emerged as a promising alternative by reducing the number of trainable parameters. LoRA is based on the assumption that weight changes during fine-tuning have a low rank. This allows the weight updates to be represented by lower-rank matrices, significantly reducing memory requirements and computational cost.

Despite LoRA's success, the restriction to low-rank updates presents a potential limitation to representational power. This can lead to performance degradation, particularly for complex tasks. The central question is therefore: Are performance differences between LoRA and traditional fine-tuning due to the reduced number of trainable parameters or the low-rank constraint?

A new method called RandLoRA attempts to answer this question and overcome LoRA's limitations. RandLoRA enables full-rank updates by using learned linear combinations of low-rank, non-trainable random matrices. The number of trainable parameters is limited by restricting optimization to diagonal scaling matrices applied to the fixed random matrices.

This approach allows leveraging the benefits of full-rank updates while maintaining parameter and memory efficiency during training. Through extensive experiments in image, language, and image-language processing, the performance of RandLoRA has been evaluated compared to LoRA and other random-based methods.

The results show that full-rank updates are beneficial for both image and language tasks. The advantage is particularly evident in image-language tasks, where RandLoRA significantly reduces, and in some cases even closes, the performance gap between traditional fine-tuning and LoRA.

Advantages of RandLoRA

RandLoRA offers several advantages over traditional fine-tuning methods and LoRA:

Increased Representational Power: By enabling full-rank updates, RandLoRA can improve the model's representational power compared to LoRA. This leads to better performance, especially for complex tasks.

Parameter Efficiency: Despite full-rank updates, RandLoRA remains parameter-efficient because only the diagonal scaling matrices are trained. This reduces memory requirements and computational cost compared to traditional fine-tuning.

Versatility: RandLoRA can be used for various tasks in image, language, and image-language processing.

Conclusion

RandLoRA represents a promising method for parameter-efficient fine-tuning of large language models. By combining full-rank updates with an efficient training procedure, RandLoRA offers the potential to close the performance gap between traditional fine-tuning and LoRA while maintaining the benefits of parameter efficiency. The results of the experiments suggest that RandLoRA can be a valuable tool for adapting LLMs to specific tasks, particularly in the field of multimodal processing.

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