Part 7/9:
While RL allows models to go beyond pure imitation—by exploring and "hill climbing" on reward functions—its current implementation remains fundamentally limited. It can discover solutions humans may not have envisioned, but it still suffers from superficial understanding. The process often results in models that are "stupid" in the sense that they lack deep reasoning or genuine comprehension.
Despite these shortcomings, research continues to push the boundaries. The speaker mentions recent papers from Google exploring "reflect and review" ideas, emphasizing the importance of memory and self-evaluation in learning. Such approaches aim to address RL's deficiencies by enabling models to review their reasoning, recall relevant information, and improve their problem-solving capabilities.