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

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