Reinforcement Fine-Tuning (RFT) can be a game-changer for improving model behavior— but getting it to work well in practice isn’t always smooth. Iteration can feel slow, evaluators can be noisy, reward signals can get “creatively interpreted,” and building the right training data often takes more trial and error than expected.

In this masterclass, we’ll share practical, production-tested recipes for making RFT faster, more reliable, and less painful. We’ll cover how to structure datasets that actually help, build evaluation loops you can trust, avoid reward hacking traps, and iterate quickly without sacrificing quality. We’ll also keep the session interactive with audience questions throughout, so we can ground the discussion in real challenges people are facing today.

Attendees will leave with clear frameworks, hard-earned lessons, and actionable next steps to bring back to their own RFT workflows.