Behavioral-Cloning Shortcut to AGI
You don't have to re-run evolution's compute to reach AGI — you can behaviorally clone everything humans already know.
Against the biological-anchors view (a Berkeley professor's claim that AGI requires reproducing all the FLOPs that went into evolution), Luan argues there is a giant shortcut: behaviorally clone everything humans already know. LLMs already solved this for written knowledge; multimodal models extend it to the visual world, heading toward a "universal byte model" where high-signal tokens come in and any combination — text, voice, image, video — comes out. The next phase recombines this cloner with the lessons of the RL era.
- Cloning existing human knowledge is vastly cheaper than rediscovering it via de-novo RL.
- Self-supervised pre-training is the data-efficient engine of the clone.
- A universal byte model learns any high-signal mapping once you have cloned the modality.
Luan's long-running debate with a Berkeley professor over what it actually takes to build AGI, revisited after the LLM/Dota-RL era at OpenAI.