GRPO Replaces Supervised Fine-Tuning as 2026 Default for LLM Reasoning: Group-Based RL Without a Reward Model

Cameron R. Wolfe·medium signal

Group Relative Policy Optimization (GRPO) has become the dominant fine-tuning technique for reasoning tasks in 2026, replacing pure SFT. The method generates multiple responses per prompt, groups them, and normalizes rewards within each group: advantage = (reward - mean) / std. Key advantage: no complex reward model required — even a simple length-based scorer works. Combined with complexity-aware data selection (achieving equivalent accuracy with 11% of training data), GRPO makes reasoning fine-tuning accessible on consumer hardware via Unsloth.