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arXiv Computation and Language By Ziqing Qiao, Xiaoyue Xu, Sikui Zhang, Yinuo Xu, Bingxiang He, Chaojun Xiao, Xu Han

Don't Stop Long-Context Continual Pre-Training Too Early: An Empirical Scaling Law

Read the original on arXiv Computation and Language →

The paper studies how long‑context continual pre‑training (LCCP) scales with token budget and model size. An empirical additive power law is derived that predicts LCCP performance across five model scales and over a hundred budget configurations, and a new Constant‑Last4 protocol is introduced for stable monitoring. Experiments show that performance continues to improve without clear saturation, even after 128K context extension and fine‑tuning, indicating that scaling LCCP yields transferable gains for downstream tasks.

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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