Interestingly this point was indicated by Karpathy last summer that RLHF is barely RL. He said it would be very difficult to apply pure reinforcement learning on open-domains. This is why RLHF are a shortcut to fill this gap but still because the reward model is trained on human vibes checks the LLM could easily game the RM by giving out misleading responses or gaming the system.

Importantly the barrier is that open domains are too complex and too undefined to have a clear reward function. But if someone cracks that — meaning they create a way for AI to self-optimize in these messy, subjective spaces — it'll completely revolutionize LLMs through pure RL.

Here's the link of the tweet: https://x.com/karpathy/status/1821277264996352246