An autoregressive language model is being trained on a single data instance. The model is provided with the input context tokens ['The', 'quick', 'brown'] and is trained to generate the target completion tokens ['fox', 'jumps']. During the backward pass for this specific training step, from which token positions will the error signals (gradients) used to update the model's weights primarily originate?

When fine-tuning an autoregressive language model on a dataset where each example consists of an input prompt and a target completion, the training loss is calculated across all tokens in the combined sequence (prompt + completion) to ensure the model understands the full context.

Example of Loss Calculation in Instruction Fine-Tuning