GRPO Reinforcement Fine-Tuning: Domain Specialization with Under 100 Examples and No Value Model

Unsloth·high signal

Group Relative Policy Optimization (GRPO), the algorithm behind DeepSeek R1, eliminates the separate critic model from PPO by generating multiple completions per prompt and computing advantages from relative quality within the group—cutting GPU memory ~30% with comparable stability. The production insight is designing verifiable reward functions: binary signals (correct JSON format, code compiles, math answer matches ground truth) dramatically outperform learned reward models for domain tasks and require fewer than 100 labeled examples. Unsloth's RL guide provides a consumer GPU walkthrough (RTX 4090, 24GB VRAM via QLoRA) for 7B model fine-tuning with GRPO, making specialized reasoning fine-tuning accessible without A100s.