The Misconception that Almost Stopped AI [How Models Learn Part 1]

3-Second Blueprint (English)

1. Perturbing individual weights in Llama 3.2 shifts next-token probabilities, revealing local slopes that guide parameter updates.
2. Projecting 2D slices of billion-parameter spaces maps loss landscapes across pre-trained and instruction-tuned language models.
3. High-dimensional geometry provides abundant escape directions, letting gradient descent tunnel into low-loss basins reliably.