Instead of local reward model, GRPO generates a group of responses for the same prompt, scores using a rule based verifier ( ideal for cases involving programmatic tasks or theorem prooving ), then calculates the advantage in comparrsion to groups average.
- highly memory efficient because you only need the policy model and the reference model in memory, not a massive secondary reward model.
- for reasoning model postraining
- optimization algorithm most used for RLVR
This is also the “hot stuff” right now (May 2026)
Breakdown
TRL from huggingface has some great methods for their GRPOTrainer
