Disentangling Curriculum Learning in NLP: Towards a Unifying Taxonomy
Despite more than a decade of curriculum learning (CL) research in NLP, the field lacks a principled account of which difficulty function or scheduler to use for a given problem. To understand what has hindered progress towards this account, we propose a fine-grained taxonomy separating difficulty evaluation from training scheduling to enable systematic analysis of CL strategies. For difficulty evaluation, we distinguish attribution source and task dependence, revealing difficulty as a perspectival concept encoding different assumptions about what makes an instance hard to learn. For scheduling, we provide the first formalisation of CL schedulers in terms of expected training contribution...
| Authors | Vanessa Toborek, Florian Seiffarth, Sebastian Müller et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 July 2026 |
| Last updated at source | 25 August 2026 |
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