Thoughts on LLMs Editing Weights: What's New? (ycao.net)

Recent discussions highlight the capability of large language models (LLMs) to read and edit neural network weights, a revelation that carries significant implications for fine-tuning and model interpretability. While some claims about these models improving reasoning abilities were critiqued for overstatement, the foundational work in interpretability has set the stage for these advancements. Importantly, LLMs demonstrate an ability to identify and rectify bugs within smaller model weights, showcasing their extended attention span compared to human cognitive limits. Experiments indicate that LLMs can visually recognize patterns in first-layer weights and detect templates in output-layer weights without needing traditional plotting tools. The significance of these findings is underscored by their connections to historical AI methodologies, including strategies for injecting advice into reinforcement learning agents. While the emergence of these capabilities in LLMs may seem unprecedented, the intricate relationship between their pretraining and post-training processes suggests that similar models could exhibit comparable skills if trained similarly. This suggests a need for further exploration into how frontier models are fine-tuned for specific tasks, and whether emergent abilities can be consistently replicated across different architectures and training regimes.