Overtraining as the path to human-like AI (www.seangoedecke.com)

In a recent extensive post titled "Human-like Neural Nets by Catapulting," the anonymous blogger Gwern proposes a radical theory about training large language models (LLMs) to achieve more human-like intelligence. He posits that current LLMs, which generally excel at specific tasks, fail to generalize like humans because they have not undergone a process he terms "grokking." This phenomenon, observed by OpenAI, occurs when training continues beyond initial improvement, allowing models to gain deeper understanding through compression of data rather than mere memorization. Gwern's argument advocates for a paradigm shift in AI training methodology: rather than training massive models on vast datasets, he suggests overtraining a smaller dataset using hyper-parameterized models. This approach could potentially lead to breakthroughs in generalization and reasoning capabilities, mimicking the learning paths seen in human development. Despite skepticism regarding the viability of this method and the substantial financial risks involved, Gwern's ambitious idea has the potential to reshape the future of AI by prompting significant advances in creating truly intelligent systems. The AI community may need to reconsider its current trajectory in model training, as exploring grokking could be a key step toward more sophisticated AI systems.