Hybrid-LoRA: Efficient Post-Training for Large Language Models
A new framework called Hybrid-LoRA selectively applies full fine-tuning to a small subset of parameters while using low-rank adaptation for the rest, bridging the performance gap between full fine-tuning and parameter-efficient methods in post-training for large language models. The approach targets complex reasoning tasks where standard LoRA underperforms, offering reduced GPU memory and training costs compared to full fine-tuning. The paper is published on arXiv under ID 2605.18822.
Key facts
- Hybrid-LoRA is a hybrid post-training framework for LLMs.
- It selectively applies full fine-tuning to a small subset of parameters.
- It uses low-rank adaptation (LoRA) for the remaining parameters.
- It aims to bridge the performance gap between FFT and PEFT.
- It targets complex reasoning tasks in post-training.
- RLVR with critic-free algorithms like GRPO and GSPO is used.
- Full fine-tuning requires substantial GPU memory and high costs.
- LoRA reduces computational costs but has a performance gap.
Entities
—