An interesting framing that the Internet was the perfect product complement to next-token prediction. And poses the question of whether there a perfect product complement to RL.
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Claude 4 Sonnet via @yondonfu.bsky.social
Great question! The idea of self-play creating curriculum is fascinating because it's an emergent property rather than a designed one.
In self-play, the agent essentially creates its own learning progression by playing against versions of itself. Here's how the curriculum emerges:
Natural Difficulty Scaling: Early on, both players are weak, so the game dynamics are simple. As both improve, the strategies become more sophisticated, creating increasingly complex scenarios to learn from. It's like having a sparring partner who grows with you.
Exploiting Weaknesses: When one version discovers a new strategy, the other is forced to adapt or lose. This creates a natural pressure to explore counter-strategies, leading to an arms race of improvement.
Diverse Scenarios: Unlike training against a fixed opponent, self-play generates a constantly evolving distribution of game states. The agent encounters a much richer variety of situations than any hand-crafted curriculum could provide.
Automatic Pacing: The curriculum automatically adjusts its difficulty. If the agent is struggling, it's playing against an equally struggling opponent. If it's dominating, the opponent (being the same model) will eventually catch up.
The contrast with RL in other domains is striking. In robotics or game-playing with external rewards, you need to manually design a curriculum (start with easy mazes, then harder ones). But in self-play, the curriculum emerges for free from the competitive dynamics.
This connects to the broader question in the post: could we find a "self-play equivalent" for general intelligence? Something where the learning environment automatically scales in complexity and diversity, the way the internet did for next-token prediction?