15 Key LLM Fine-Tuning Techniques: LoRA and GRPO Explained

Fine-tuning large language models (LLMs) is crucial for companies and developers seeking to deploy generative artificial intelligence (AI) applications. However, the high computing costs and hardware requirements of full-parameter fine-tuning have long impeded adoption among small and medium-sized enterprises and individuals. The industry continues to explore more efficient model alignment and fine-tuning methods to reduce training resource consumption, improve output quality, overcome hardware constraints and accelerate the development of customized AI applications.

AI technical writer Akshay Pachaar outlined 15 mainstream LLM fine-tuning techniques on social media platform X on July 10, 2026. Among them, LoRA is known for its low memory requirements and can freeze pretrained weights, allowing fine-tuning on a single graphics card with 24GB of VRAM. The list also covers alignment-training methods such as GRPO, which is used by DeepSeek and can effectively lower training costs while significantly improving computational quality.