CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models

• URL: http://arxiv.org/abs/2510.13008v1
• Date: Tue, 14 Oct 2025 21:56:03 GMT
• Title: CurLL: A Developmental Framework to Evaluate Continual Learning in Language Models
• Abstract summary: We introduce a comprehensive continual learning dataset and benchmark (CurlL) grounded in human developmental trajectories from ages 5-10.<n>CurlL spans five developmental stages (0-4) covering ages 5-10, supported by a skill graph that breaks down broad skills into smaller abilities.<n>We generate a 23.4B-token synthetic dataset with controlled skill progression, vocabulary complexity, and format diversity.
• Score: 6.333098807183056
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We introduce a comprehensive continual learning dataset and benchmark (CurlL) grounded in human developmental trajectories from ages 5-10, enabling systematic and fine-grained assessment of models' ability to progressively acquire new skills. CurlL spans five developmental stages (0-4) covering ages 5-10, supported by a skill graph that breaks down broad skills into smaller abilities, concrete goals, and measurable indicators, while also capturing which abilities build on others. We generate a 23.4B-token synthetic dataset with controlled skill progression, vocabulary complexity, and format diversity, comprising paragraphs, comprehension-based QA (CQA), skill-testing QA (CSQA), and instruction-response (IR) pairs. Stage-wise token counts range from 2.12B to 6.78B tokens, supporting precise analysis of forgetting, forward transfer, and backward transfer. Using a 135M-parameter transformer trained under independent, joint, and sequential (continual) setups, we show trade-offs in skill retention and transfer efficiency. By mirroring human learning patterns and providing fine-grained control over skill dependencies, this work advances continual learning evaluations for language models.

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