SkyLadder: Better and Faster Pretraining via Context Window Scheduling
Authors: name: Tongyao Zhu, name: Qian Liu, name: Haonan Wang, name: Shiqi Chen, name: Xiangming Gu, name: Tianyu Pang, name: Min-Yen Kan

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 at https://github.com/sail-sg/SkyLadder.