Hands-On: Fine-Tuning a Reasoning Model with Unsloth and HuggingFace TRL
~20 min read
The book's actual GRPO walkthrough: load Qwen3-4B-Base with Unsloth, configure LoRA, prepare the Open R1 Math dataset, define the 4 reward functions, and train with HuggingFace TRL's GRPOConfig and GRPOTrainer.
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Key points
- •The book's recipe uses 2 libraries together: UnslothAI for efficient fine-tuning, HuggingFace TRL for applying GRPO itself
- •5 steps: load the model (Qwen3-4B-Base), configure LoRA, prepare the Open R1 Math dataset, define the 4 reward functions, train with GRPOConfig/GRPOTrainer
- •LoRA matters especially here since GRPO's group generation (multiple candidates per prompt) makes full fine-tuning even more expensive
- •The dataset format bundles a reasoning-focused system prompt, the question, and the expected answer the reward functions check against
- •GRPOTrainer handles group generation, reward aggregation, and the GRPO loss internally — the book's own before/after comparison shows real reasoning improvement