LLMs are not superhuman at reading and writing neural network weights (www.seangoedecke.com)

A recent paper discusses the capability of the GPT-6 Astra model to interact with neural network weights, raising questions about the supposed superhuman abilities of advanced LLMs. Astra effectively identified the games associated with a small neural network by analyzing its weights and significantly improved the performance of a 0.8 billion parameter Qwen model in mathematical tasks by editing its weights directly. However, the analysis suggests that while Astra's achievements are noteworthy, they are not unprecedented or indicative of a radical shift in AI research methods. In fact, a skilled human researcher could perform the same tasks, such as identifying game patterns from weights and addressing model looping issues. The implications for the AI/ML community are nuanced. While Astra's direct weight modifications are impressive, they do not supplant traditional training methods, which remain the most effective means of model improvement. The paper reinforces the notion that intelligent LLMs might bolster AI research by enhancing training processes rather than replacing the foundational techniques of writing and compiling code. Ultimately, while advancements like Astra showcase the evolving capabilities of LLMs, they also highlight the continued importance of human ingenuity and established methods in the field.