Modern Reinforcement Learning (RL), Part 1: How RL Powers Generative AI скачать в хорошем качестве

Modern Reinforcement Learning (RL), Part 1: How RL Powers Generative AI

Reinforcement Learning (RL) isn’t just for robots anymore — it’s transforming how Generative AI models learn, align, and evolve. In Part 1 of the Modern Reinforcement Learning Series, we explore how RL techniques are shaping today’s large language models and creative AI systems. You’ll learn about: ✅ RLHF (Reinforcement Learning from Human Feedback) – the foundation behind ChatGPT-style alignment ✅ PPO (Proximal Policy Optimization) – the algorithm that stabilizes training ✅ DPO (Direct Preference Optimization) – a simpler, more efficient successor to RLHF ✅ DivPO (Diverse Preference Optimization) – balancing quality and creativity in model behavior ✅ GFlowNets (Generative Flow Networks) – a breakthrough framework for diverse structured generation By the end of this episode, you’ll understand how reinforcement learning drives the next generation of AI systems, from reward modeling to diversity-driven policy optimization. 📍 Next in Series: Part 2 — RL for Agentic AI 💡 Want to go deeper? If you’re building AI products, scaling LLM systems, or need 1-on-1 mentoring or consultation on AI strategy, check out www.sammokhtari.com/services 📺 Subscribe for upcoming parts on RL, alignment, and autonomous agents. 🔗 Follow me on LinkedIn and YouTube for updates and insights.