Walk Through Next-Token Training
Trace how next-token prediction creates a learning signal for transformer weights.
Explain how next-token prediction creates a useful learning signal. Predict, measure loss, backpropagate gradients, update weights, repeat at scale. The common trap is saying the model memorizes the next word in each sentence. Training changes shared weights that generalize across many contexts. Step 1: Predict Given The invoice is due on, the model outputs probabilities over the vocabulary. The output is a distribution, not a single stored answer. Step 2: Check loss If the actual next token is Friday, loss is lower when the model assigned high probability to Friday and higher when it did not. The objective turns prediction…
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