LLM reinforcement fine-tuning platform to improve LLM output
Predibase has released the first Reinforcement Fine-Tuning platform, promising a groundbreaking approach to customizing LLMs using reinforcement learning. Use RFT to train open-source LLMs that outperform GPT-4, even when labeled data is limited.
Tuning LLMs just got 100x easier—no massive datasets, no endless prompt engineering. With Predibase RFT, you can fine-tune models to outperform GPT-4 with just a dozen labeled examples. Yes, really.
💡 Why is this game-changing? ✅ No More Labeling Bottlenecks: Get performance that beats commercial LLMs without massive datasets. ⚡ Rapid Iteration: Go from idea to deployment faster than ever. ⚙️ Turbocharged Inference: See up to 3x faster performance for reasoning models using Turbo LoRA speculative decoding. 🔒 Enterprise-Ready: Deploy in your VPC or on our cloud with full security.
Inspired in part by the GRPO framework behind DeepSeek-R1, we built RFT because we were tired of seeing teams unable to fine-tune models due to a lack of labeled data. Now, AI teams can customize models faster and with higher accuracy without requiring 1,000s of rows of labeled data—and it's already delivering 20%+ better performance than GPT-4 in specialized tasks.
