Part 4/10:
Historically, the pre-training phase has absorbed a large portion of computational resources. However, the emerging vision indicates a future where RL compute could dominate. In this scenario, the amount of compute allocated to reinforcement learning will outstrip that used in pre-training, facilitating a robust cycle of self-improvement.
Dr. Jim Fan from Nvidia is also reshaping this conversation by pointing out the urgent need to rethink compute distribution in AI training. This realization emphasizes that effective reinforcement learning, possibly detached from human instruction, may unlock transformative advancements in how AI accomplishes tasks.