SkyLadder: Better and Faster Pretraining via Context Window Scheduling

Recent advancements in LLM pretraining have featured ever-expanding context windows to process longer sequences. However, our pilot study reveals that models pretrained with shorter context windows consistently outperform their long-context counterparts under a fixed token budget. This finding motivates us to explore an optimal context window scheduling strategy to better balance long-context capability with pretraining efficiency. To this end, we propose SkyLadder, a simple yet effective approach that implements a short-to-long context window transition. SkyLadder preserves strong standard benchmark performance, while matching or exceeding baseline results on long context tasks. Through extensive experiments, we pre-train 1B-parameter models (up to 32K context) and 3B-parameter models (8K context) on 100B tokens, demonstrating that SkyLadder yields consistent gains of up to 3.7% on common benchmarks, while achieving up to 22% faster training speeds compared to baselines. The code is atthis https URL.

@article{zhu2025_2503.15450,
  title={ SkyLadder: Better and Faster Pretraining via Context Window Scheduling },
  author={ Tongyao Zhu and Qian Liu and Haonan Wang and Shiqi Chen and Xiangming Gu and Tianyu Pang and Min-Yen Kan },
  journal={arXiv preprint arXiv:2503.15450},
  year={ 2025 }
}