Reinforcement Fine-Tuning: The Cherry of LLMs

Reinforcement Fine-Tuning (RFT) adds a final layer of precision to large language models by letting them learn from feedback loops, going beyond what supervised fine-tuning (SFT) alone can achieve. As models mature, RFT becomes essential for unlocking performance gains in complex, verifiable tasks.

Key Ideas

RFT enhances model behavior through automated grading: By using rule-based validators, code tests, or even LLMs as scorers, models receive continuous feedback during training. This feedback nudges them toward better task alignment and more reliable output—especially when manual labeling is inefficient or impractical.
Success hinges on well-defined tasks and robust reward design: RFT thrives when the task is scoped with clear success criteria and the model already performs reasonably well. A consistent, scalable grading mechanism provides the signal necessary to guide learning—transforming capable models into highly specialized, domain-aligned systems.