[RL Fine-Tuning] From RLHF to GRPO: The Evolution and Optimization of AI LLM Models Alignment.

In the landscape of Artificial Intelligence, we’ve spent years marveling at the sheer scale of Foundation Models—the trillions of parameters and the massive datasets. But today, the conversation has shifted. It’s no longer just about how much a model knows; it’s about how it behaves.

How do we take a raw neural network and align it with human intent, safety, and complex reasoning? We’re moving beyond the era of simple imitation. Today, we are deep-diving into the Evolution and Optimization of Foundation Model Alignment Algorithms."

In this episode, we trace the lineage of alignment—from the foundational days of Supervised Fine-Tuning (SFT) to the breakthrough of Reinforcement Learning from Human Feedback (RLHF). We’ll explore how Direct Preference Optimization (DPO) turned a complex reinforcement learning problem into a sleek, efficient classification task, and why the latest shift toward Group Relative Policy Optimization (GRPO) is finally cracking the code on complex reasoning for math and software engineering.

Whether you are an AI researcher, a developer, or just curious about the mechanisms driving the 'ghost in the machine,' we’re breaking down the game theory, the offline optimization, and the unified architectures that are turning these models into safe, capable digital agents.