Compete Guide to Reinforcement Fine-Tuning
Everything you need to know about RFT and training custom reasoning models—no labeled data required
Unlock GPT-4-level performance—without GPT-4 costs.
Reinforcement Fine-Tuning (RFT) is rewriting the rules for open-source LLMs. This hands-on guide shows you how to use RFT to train smarter, faster models with just a handful of examples—no massive labeled datasets required.
You’ll learn:- Why RFT beats supervised fine-tuning when data is scarce
- How DeepSeek-R1 outperformed closed models through self-improvement
- Real-world benchmarks and how to get 2–4x faster inference with Turbo LoRA
- Step-by-step tutorial: train a model to write GPU kernels from scratch
Whether you’re a machine learning engineer or AI platform leader, this guide is packed with real experiments, cost-saving tips, and a clear roadmap to build reasoning models that adapt, evolve, and outperform.
Download the Complete Guide to RFT and start fine-tuning like it’s 2025.