Four Quadrants of Difficulty: A Simple Categorisation and its Limits

• URL: http://arxiv.org/abs/2601.01488v1
• Date: Sun, 04 Jan 2026 11:31:51 GMT
• Title: Four Quadrants of Difficulty: A Simple Categorisation and its Limits
• Authors: Vanessa Toborek, Sebastian Müller, Christian Bauckhage,
• Abstract summary: We propose a four-quadrant categorisation of difficulty signals -- human vs. model and task-agnostic vs. task-dependent.<n>We find that task-agnostic features behave largely independently and that only task-dependent features align.<n>These findings challenge common Curriculum Learning intuitions and highlight the need for lightweight, task-dependent difficulty estimators.
• Score: 4.304007567113229
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Curriculum Learning (CL) aims to improve the outcome of model training by estimating the difficulty of samples and scheduling them accordingly. In NLP, difficulty is commonly approximated using task-agnostic linguistic heuristics or human intuition, implicitly assuming that these signals correlate with what neural models find difficult to learn. We propose a four-quadrant categorisation of difficulty signals -- human vs. model and task-agnostic vs. task-dependent -- and systematically analyse their interactions on a natural language understanding dataset. We find that task-agnostic features behave largely independently and that only task-dependent features align. These findings challenge common CL intuitions and highlight the need for lightweight, task-dependent difficulty estimators that better reflect model learning behaviour.

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