Gwern proposes overtraining giant models on small datasets to unlock human-like AI

Gwern's essay builds on his established credibility in AI prediction. He published "The Scaling Hypothesis" in 2020, immediately after GPT-3's release, correctly anticipating the trillion-dollar GPU cluster arms race and continued scaling through the decade—predictions made two years before ChatGPT and the broader AI boom. This track record lends weight to his current proposal, even though it contradicts the dominant scaling orthodoxy of recent years.

The post identifies a genuine empirical gap: LLMs demonstrably fail to generalize as broadly as humans, despite matching human performance in narrow domains. The question Gwern raises—why neural networks should be capable of any generalization but stop at the level of current LLMs—is difficult to dismiss on pure principle. The open question is whether language and reasoning contain the kind of deep, discoverable rules that grokking has demonstrated in simple mathematical domains, or whether human generalization relies on architectural features neural networks cannot replicate.

The practical obstacle is as much cultural as technical. A lab would need to commit billions of dollars and engineering resources to an experiment that offers no visible progress for an extended period. The 2024 plateau in pure scaling (where larger GPT-4 variants underperformed) and the subsequent success of reasoning and automated RL suggest the field has already abandoned simple scaling. Whether it will embrace Gwern's radically different approach remains an open question.