Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting
Large language models (LLMs) drift out of date the moment their pretraining ends, yet retraining from scratch is prohibitively expensive. Continued pretraining (CPT) is the natural remedy, but it is typically evaluated through a continual learning lens that assumes disjoint data streams. This is a poor fit for time-incremental updates on web-scale crawls, where successive snapshots share substantial URL overlap by design. We study time-incremental CPT in this realistic regime: continued pretraining on FineWeb-Edu dumps drawn strictly from after each model's knowledge cutoff, evaluated across six open-weight models spanning three families (OLMo2, Llama-3.1/3.2, Gemma-3-1B) and four paramet...
| Authors | Fırat Öncel, Salman Hussain Ali, Mirco Ravanelli et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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