SFT Objective as Maximizing Joint Log-Probability of Concatenated Sequences

In a common fine-tuning strategy, a prompt and its desired completion are concatenated into a single sequence (e.g., [prompt_tokens, completion_tokens]). The language model is then trained on this full sequence, but the training loss is calculated only for the model's predictions on the completion tokens. What is the most accurate analysis of the primary purpose of this specific loss calculation method?

During supervised fine-tuning, if a model is trained on concatenated [input, output] sequences and the training loss is calculated across the entire sequence (both input and output tokens), the model is still being optimized primarily to improve its conditional generation capabilities for the given input.

Loss Masking via Forward and Backward Passes in SFT