What Large‑Model Training Actually Optimizes: Parameters, Attention, and Knowledge Explained

What training optimizes

Training optimizes the weight parameters of a neural network. Back‑propagation and gradient descent only modify the parameter matrix; knowledge, attention, and structure are not directly trained objects.

Training = continuously optimizing parameters to reduce the loss function.

Is attention trained directly?

Attention is a mechanism realized by parameters. In a Transformer the following are parameters:

Q/K/V projection matrices

Linear layers of multi‑head attention

FFN, LayerNorm, RoPE‑related parameters

During training the model learns to make the attention mechanism useful, to focus on relevant tokens, and to assign different heads to distinct semantic functions (syntax, coreference, logic, etc.).

Attention = structure; parameters = its implementation.

Where is knowledge stored?

Knowledge is encoded in all parameters, including factual knowledge (e.g., “Paris is the capital”), language patterns (grammar, logic, common sense), and world knowledge, reasoning patterns, causal relationships. These are not stored in an explicit database but are distributed and compressed within the weight matrices.

Knowledge = statistical regularities and semantic information embedded in the parameters.

Relationship summary

Parameters : the model’s sole learnable carrier.

Attention : the mechanism for capturing dependencies.

Knowledge : the content and capabilities the model ultimately learns.

Training = using data to optimize parameters, enabling the attention mechanism to work effectively, so the model acquires knowledge and reasoning ability.

Standard interview answer

Large‑model training fundamentally optimizes neural‑network parameters via gradient descent, updating the weight matrix so that the self‑attention mechanism can model long‑range dependencies and semantic relations, ultimately allowing the model to encode and store world knowledge, language patterns, and reasoning ability within its parameters. The three form a hierarchy: carrier → mechanism → content.