Originally published on AI Tech Connect.
What you need to know Reinforcement fine-tuning teaches a model to think, not to copy. Instead of imitating labelled examples the way supervised fine-tuning does, RFT hands the model a reward signal for a verifiable outcome and lets it discover its own strategy. It is the right tool when correctness is checkable — maths, code that must pass tests, structured extraction, tool-use that either works or does not. GRPO is the algorithm everyone is using now. Group Relative Policy Optimization is a leaner PPO: it drops the separate critic model and instead samples a group of answers per prompt and scores each against the group's own average. That single change roughly halves the memory and makes RFT feasible on one GPU. The reward function is the entire job. A good reward combines a verifier…
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