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DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
arXiv – CS AI|Selcuk Gurses, Aozhong Zhang, Yanxia Deng, Xun Dong, Xin Li, Naigang Wang, Penghang Yin, Zi Yang||1 views
🤖AI Summary
DiaBlo introduces a new Parameter-Efficient Fine-Tuning (PEFT) method that updates only diagonal blocks of weight matrices in large language models, offering better performance than LoRA while maintaining similar memory efficiency. The approach eliminates the need for low-rank matrix products and provides theoretical guarantees for convergence, showing competitive results across various AI tasks including reasoning and code generation.
Key Takeaways
- →DiaBlo updates only diagonal blocks of selected model weight matrices, avoiding the computational overhead of low-rank matrix products used in LoRA.
- →The method provides theoretical guarantees showing superior expressiveness compared to LoRA under mild low-rank conditions.
- →DiaBlo maintains comparable memory efficiency and training speed to existing PEFT methods while achieving better performance.
- →Extensive experiments demonstrate strong performance across commonsense reasoning, arithmetic reasoning, code generation, and safety alignment tasks.
- →The approach offers more stable and robust convergence without requiring auxiliary initialization schemes or customized optimization strategies.
#llm#fine-tuning#peft#machine-learning#model-optimization#parameter-efficiency#deep-learning#ai-research
Read Original →via arXiv – CS AI
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