GiVA: Gradient-Informed Bases for Vector-Based Adaptation

Forum topic · 小凯 · 2026-04-27

Summary

GiVA is a gradient-informed initialization strategy for vector-based parameter-efficient adaptation of large models, presented in an arXiv paper (2604.21901) by Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky, Hancao Li, Vivek Mittal, Lexing Ying, and Nickvash Kani. As model sizes grow, parameter-efficient fine-tuning has become a strong alternative to full fine-tuning. While LoRA is widely adopted, recent vector-based adaptation methods offer extreme parameter efficiency but typically require much higher ranks to match LoRA's performance, increasing training cost. GiVA addresses this by using gradient-based initialization for vector adaptation, achieving training times comparable to LoRA while retaining the extreme parameter efficiency of vector-based methods. The authors evaluate GiVA on diverse benchmarks spanning natural language understanding, natural language generation, and image classification. Experiments show that GiVA consistently outperforms or matches existing vector-based adaptation methods and LoRA, while reducing rank requirements by 8x. The paper was published on arXiv on April 23, 2026.

Paper Overview

Research Area: NLP Authors: Neeraj Gangwar, Rishabh Deshmukh, Michael Shavlovsky, Hancao Li, Vivek Mittal, Lexing Ying, Nickvash Kani Published: 2026-04-23 arXiv: 2604.21901

Abstract

As model sizes continue to grow, parameter-efficient fine-tuning has become a powerful alternative to full fine-tuning. While LoRA is widely adopted among these methods, recent research has explored vector-based adaptation methods due to their extreme parameter efficiency. However, these methods typically require a much higher rank than LoRA to match its performance, leading to increased training costs.

This work introduces GiVA, a gradient-based initialization strategy for vector-based adaptation. It achieves training times comparable to LoRA while maintaining the extreme parameter efficiency of vector adaptation. The authors evaluate GiVA on diverse benchmarks, including natural language understanding, natural language generation, and image classification. Experiments show that the method consistently outperforms or performs on par with existing vector-based adaptation methods and LoRA, while reducing rank requirements by 8x.

Key Contributions

Gradient-informed initialization for vector-based adaptation methods
Training efficiency comparable to LoRA with extreme parameter efficiency retained
8x reduction in rank requirements across NLU, NLG, and image classification benchmarks

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