Part 2/9:
According to the speaker, reinforcement learning is far worse than many people perceive. It involves a trial-and-error approach where an agent tries numerous attempts in parallel to solve a problem. For example, when solving a math problem, an AI might generate hundreds of different solutions, then check which ones lead to the correct answer. The key issue is how RL reinforces certain behaviors: it boosts the likelihood of actions that led to success, regardless of whether those actions were genuinely the right strategies or just coincidental pathways to the solution.