Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Training
Training general-purpose Audio Large Language Models (ALLMs) across diverse datasets is essential for holistic audio understanding, yet it faces significant challenges due to dataset heterogeneity, which often leads to conflicting gradients and slow convergence. Despite its impact, how to explicitly manage this heterogeneity during training remains underexplored, with current practices relying primarily on uniform mixture. In this work, we analyze multi-dataset AudioQA training from a convergence perspective and propose Grouped Sequential Training (GST). GST strategically organizes datasets into affinity-aware groups and introduces them via a progressive scheduling protocol, effectively b...
| Authors | Yanru Wu, Jianning Wang, Chongxin Gan et al. |
|---|---|
| Primary category | cs.SD |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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