Reinforcement Fine-Tuning (RFT): Why It's the Future of LLM Training Without Labels скачать в хорошем качестве

Reinforcement Fine-Tuning (RFT): Why It's the Future of LLM Training Without Labels

🔔 SUBSCRIBE for the latest on LLM fine-tuning, AI scaling, and reinforcement learning hacks! 👉   / @predibase   What if you could fine-tune a high-performing LLM without needing thousands of labeled examples? Welcome to the era of Reinforcement Fine-Tuning (RFT) — the breakthrough technique that’s redefining how machine learning models are trained, deployed, and scaled. 🔗 Watch the full video and get a notebook link:    • 🔥 Live Demo: Reinforcement Fine-Tunin...   In this video, Predibase CEO Dev Rishi ​ ⁨‪@DevIntheDetails‬ shares why 90% of ML is labeling data... and the other 10% is complaining about it. But what if you didn’t need to label at all? He breaks down: 🧠 Why RFT is game-changing for startups, enterprises, and researchers ⚙️ The three exact scenarios when RFT outperforms SFT 💥 Real-world use cases in code generation, math reasoning & multi-step logic 📊 How to decide between RLHF, RFT, and traditional fine-tuning 🔍 How to score outputs with reward functions instead of manual labels Whether you’re working with limited data, building agentic systems, or just want to boost accuracy with fewer resources — this video gives you the insights, framework, and criteria to know if RFT is right for your project. 👉 Try it free: https://pbase.ai/4brbC8u 👉 Schedule a live demo: https://pbase.ai/41FZKfy 👉 Learn more: https://pbase.ai/Intro-RFT-platform #ai #machinelearning #reinforcementlearning #reinforcementfinetuning #rft #llms #finetuning #SFTvsRFT #lora #rlhf #mlengineering #datascience #opensourcellms #customllm #aitraining #llmops #mlinfrastructure #automl #modelcustomization #RewardFunctions #nolabelsneeded #aioptimization #grpo #deeplearning