Seonglae ChoThe process of artificial neural networks extracting features from data and abstracting separation in each neuron.
Updates all parameters while gaining general comprehension ability
Dataset for AI are three types
- Background information - Pretraining
- Problems with solution - SFT
- Practice problems - Reinforcement Learning
Pre Training Notion
How training process and loss value is related to neural network’s ability
Perhaps the most striking phenomenon the Anthropic have noticed is that the learning dynamics of toy models with large numbers of features appear to be dominated by "energy level jumps" where features jump between different feature dimensionalities.
Procedural Knowledge in Pretraining
We observe that code data is highly influential for reasoning. StackExchange as a source has more than ten times more influential data in the top and bottom portions of the rankings than expected if the influential data was randomly sampled from the pretraining distribution. Other code sources and ArXiv & Markdown are twice or more as influential as expected when drawing randomly from the pretraining distribution