GiVA: Gradient-Informed Bases for Vector-Based Adaptation

📰 ArXiv cs.AI

Learn how GiVA improves vector-based adaptation for efficient fine-tuning of large models

advanced Published 25 Apr 2026

Action Steps

Implement GiVA using gradient-informed bases to reduce training costs
Compare GiVA with LoRA and other vector-based adaptation methods for performance evaluation
Apply GiVA to large-scale models for parameter-efficient fine-tuning
Test GiVA on various downstream tasks to assess its adaptability
Configure GiVA to balance rank and performance for optimal results

Who Needs to Know This

Researchers and engineers working on large language models can benefit from GiVA to improve fine-tuning efficiency

Key Insight

💡 GiVA uses gradient-informed bases to improve vector-based adaptation, reducing training costs and increasing efficiency

Key Takeaways

Learn how GiVA improves vector-based adaptation for efficient fine-tuning of large models

Full Article

Title: GiVA: Gradient-Informed Bases for Vector-Based Adaptation

Abstract:
arXiv:2604.21901v1 Announce Type: cross Abstract: As model sizes continue to grow, parameter-efficient fine-tuning has emerged as 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 substantially higher ranks than LoRA to match its performance, leading to increased training costs. This work introduces GiVA, a gr

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