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arXiv Computation and Language By F{\i}rat \"Oncel, Salman Hussain Ali, Mirco Ravanelli, Cem Subakan, \c{C}a\u{g}atay Y{\i}ld{\i}z

Time-Incremental Continued Pretraining of LLMs: Knowledge Updates Without Catastrophic Forgetting

Read the original on arXiv Computation and Language →

The paper investigates time‑incremental continued pretraining (CPT) of large language models using web‑scale data that overlaps across snapshots. Across six open‑weight models, CPT improves factual recall without catastrophic forgetting, while the cost is negligible and data quality outweighs quantity. The study identifies optimal learning rates, shows LoRA can match full CPT, and demonstrates that CPT gains transfer to fine‑tuned models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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