Build Hour: Reinforcement Fine-Tuning

Reinforcement fine-tuning (RFT) is positioned as the most direct way to improve an LLM’s reasoning behavior when the model already has the needed facts but still struggles to apply them correctly. Instead of training on labeled “right answers,” RFT uses a grader—implemented as a rubric or scoring function—to evaluate multiple candidate responses produced from the same input. The system then learns from those graded trajectories, making it especially useful for policy compliance, legal reasoning, and medical workflows where correctness depends on how the model reasons, not just what it knows.

The session frames model customization around two levers: improving knowledge (via prompting and retrieval augmented generation) and improving reasoning (where fine-tuning fits). Fine-tuning is treated as an investment that should come only after teams squeeze value from prompting and RAG. Within OpenAI’s platform, three fine-tuning approaches are contrasted: supervised fine-tuning (prompt/answer pairs), preference fine-tuning (better vs. worse examples), and reinforcement fine-tuning, which relies on graders to score outputs. RFT is highlighted as data efficient—often requiring only tens to hundreds of examples—because each training example yields many sampled reasoning paths and thus a richer learning signal than a single labeled target.

A live walkthrough demonstrates how to set up an RFT task using a multilingual legal classification problem based on EuroVoc level one categories. The dataset contains 7,000 samples across 23 languages, with each text assigned to one or more of 21 broad thematic classes. The demo emphasizes that data quality and sampling strategy matter: training on imbalanced label distributions can lead to “reward hacking,” where the model exploits skewed data by over-predicting frequent categories. To counter this, the workflow includes balanced sampling for the training split.

Evaluation centers on precision, recall, and a single composite F1 score because the reinforcement system needs one grade per sample. Precision measures how many predicted labels are correct; recall measures how many expected labels were recovered. F1 weights the weaker of the two metrics more heavily, and the session notes that alternative composites like F2 can be used when recall should dominate.

Under the hood, the grader is implemented as executable Python scoring code (with edge-case handling to keep scores valid). Prompt optimization is treated as critical because the same prompt structure used during training should match inference-time behavior. The demo uses structured outputs to guarantee consistent response formatting, enabling reliable grader application. It also stresses “apples to apples” evaluation by reusing the same grader objects across the evaluation platform and the RFT training pipeline.

The training process is monitored through reward curves and variance studies. Multiple runs per sample reveal how stochastic outputs affect learning signal; the system learns to pull the mean score upward toward the best observed outcomes. Additional charts track precision/recall trade-offs across checkpoints, reasoning-token behavior, and latency/grade errors—tools meant to diagnose overfitting, checkpoint selection, and cost-performance trade-offs. A comparison against baseline models (including GPT4.1 and O4 mini) shows the fine-tuned model improving both precision and recall, yielding a higher F1 score.

A customer spotlight from Accordance extends the theme to real-world tax strategy and optimization. Their approach emphasizes task selection (reasoning-heavy, objectively gradable problems), careful data curation (small, high-quality subsets matter because individual low-quality rows can harm RFT), and grader design that is continuous and stratified to avoid rewarding superficial guessing. They report over 40% improvements on an industry evaluation set called tax bench.

The Q&A distills the requirements: RFT works best with reasoning models, formal rubrics that can produce continuous rewards, and nonzero baseline performance. Noisy data is addressed by cleaning and clarifying the task environment. Finally, cost and latency trade-offs are acknowledged: RFT can be used to extract frontier-level performance from cheaper models, but the number of reasoning tokens and the overhead of training and experiments can be difficult to control, making production volume a key factor in whether the investment pays off.