An AI research team is pre-training a language model to be highly proficient at understanding grammatical structure and subtle word choices. They are debating between two self-supervised training objectives using a large corpus of 500-word articles:

Objective A: The model reads an entire 500-word article and predicts its primary topic from a list of 10 categories (e.g., 'Sports', 'Technology', 'Politics').

Objective B: The model reads a 500-word article where a few words have been subtly replaced with incorrect but grammatically plausible alternatives. The model's task is to examine every single word and decide if it was part of the original text or a replacement.

Based on the team's goal, which training objective is likely to be more efficient for learning the desired skills? Justify your choice by comparing the nature and density of the learning signals provided by each objective.