Summary of Loko: Low-rank Kalman Optimizer For Online Fine-tuning Of Large Models, by Hossein Abdi et al.
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models
by Hossein Abdi, Mingfei Sun, Andi Zhang, Samuel Kaski, Wei Pan
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to parameter-efficient fine-tuning, casting it as an optimal filtering/state estimation problem. The authors propose Low-Rank Kalman Optimizer (LoKO), which leverages low-rank decomposition and diagonal approximation of covariance matrices to significantly reduce computational complexity from quadratic to linear in the number of trainable parameters. LoKO converges with fewer iterations and yields better performance models compared to commonly used optimizers with Low-Rank Adaptation (LoRA) in both image classifications and language tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to train big AI models by using a new way to fine-tune them. The authors create an optimizer called LoKO, which is based on a mathematical problem-solving technique called the Kalman filter. This approach reduces the amount of computation needed, making it faster and more efficient. The results show that LoKO can achieve better performance with fewer training steps compared to other optimizers. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Parameter efficient
