With RFT, open models can match or even surpass the quality of closed frontier models, while running up to 10× faster. You only need to specify an evaluator function that grades model outputs and provide a few labeled examples—no infrastructure setup required.
Quality
Frontier model quality across key use cases
Use RFT to train models that execute accurate function calls, generate clean, compilable code, outperform base models in creative writing judged by LLMs, and solve math problems with over 90% accuracy using reward shaping.
Reward Functions
Build Custom Reward Functions
Use Reward-kit to define exactly how model outputs should be scored—match function calls, validate code execution, or use an LLM as judge. Write custom evaluators in Python, explore ready-made examples, and contribute your own to the open-source library.
