Embeddings evolve during training as the model adjusts their vector values to capture meaningful patterns in the data. Initially, embeddings are randomly initialized, often using methods like Gaussian noise or pretrained values. As training progresses, the model updates these vectors via backpropagation, guided by the loss function. For example, in a language model, word embeddings start as arbitrary points in high-dimensional space but gradually cluster based on semantic or syntactic similarity. Words like “dog” and “cat” might move closer together, while “car” and “tree” diverge. These updates happen incrementally, with gradients nudging embeddings toward configurations that minimize prediction errors.
In the middle stages of training, embeddings begin to encode more nuanced relationships. For instance, in recommendation systems, user and item embeddings might start reflecting user preferences or item attributes. If a user interacts with sci-fi movies, their embedding shifts toward vectors representing films like “Star Wars” and away from unrelated genres. Similarly, in transformer models, positional embeddings adjust to represent token order more effectively. During this phase, the model often discovers intermediate features—like part-of-speech tags in language tasks or texture patterns in image models. These adjustments are not uniform; some dimensions in the embedding space may stabilize early, while others continue to change as the model refines its understanding.
By the final stages, embeddings typically stabilize, with minor tweaks as the model converges. For example, in word2vec, the famous analogy “king - man + woman ≈ queen” emerges because the embeddings now reliably encode gender and royalty relationships. In contrast, poorly trained embeddings might fail to separate overlapping concepts, like mixing “bank” (financial) and “bank” (river) meanings. The quality of this evolution depends on factors like dataset size, model architecture, and training objectives. Developers can monitor embedding changes using visualization tools like t-SNE or PCA to ensure they align with expected semantic or structural patterns, adjusting hyperparameters like learning rate if progress stalls.