LLM Fine-Tuning Methods: SFT, LoRA, QLoRA, RFT, and Distillation Explained

The article outlines various methods for fine-tuning large language models, focusing on practical applications and tool choices. Supervised Fine-Tuning (SFT) is presented as a starting point, requiring labeled input-output pairs. LoRA and QLoRA are discussed as efficient alternatives that train adapter weights instead of the entire model, with Unsloth noted for accelerating these processes. Reinforcement Fine-Tuning (RFT), particularly using GRPO and its variants like DAPO++ and Dr.GRPO, is recommended for tasks where judging output quality is easier than demonstrating it, such as agentic behaviors. Distillation is also covered as a technique to transfer knowledge from large models to smaller, more efficient ones. AI

IMPACT Provides practical guidance for developers on selecting and implementing LLM fine-tuning methods, impacting model customization and efficiency.

RANK_REASON The article provides an explanatory overview of LLM fine-tuning techniques and tools, rather than announcing a new release or significant industry event.

Read on dev.to — LLM tag →

• DAPO++
• DeepSeekMath
• DeepSeek-R1
• Grpo
• LoRA+
• Overmind
• Proximal Policy Optimization
• QLoRA
• reinforcement learning from human feedback
• supervised fine-tuning
• Thinking Machines Lab
• Tinker
• Unsloth

• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →