Title: Pre-training LLM without Learning Rate Decay Enhances Supervised Fine-Tuning
Primary area: foundation or frontier models, including LLMs
Keywords: Learning rate schedules, Large language models (LLMs)

Abstract: We investigate the role of learning rate scheduling in the large-scale pre-training of large language models, focusing on its influence on downstream performance after supervised fine-tuning (SFT).
Decay-based learning rate schedulers are widely used to minimize pre-training loss.
However, despite their widespread use, how these schedulers affect performance after SFT remains underexplored.
In this paper, we examine Warmup-Stable-Only (WSO), which maintains a constant learning rate after warmup without any decay.
Through experiments with 1B and 8B parameter models, we show that WSO consistently outperforms decay-based schedulers in terms of performance after SFT, even though decay-based schedulers may exhibit better performance after pre-training.
The result also holds across different regimes with mid-training and over-training.
Loss landscape analysis further reveals that decay-based schedulers lead models into sharper minima, whereas WSO preserves flatter minima that support adaptability.
These findings indicate that applying LR decay to improve pre-training metrics may compromise downstream adaptability.
Our work also provides practical guidance for training and model release strategies, highlighting that pre-training models with WSO enhances their adaptability for downstream tasks.