Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning
Model collapse is a central challenge in learning from synthetic data: as later-generation large language models (LLMs) are trained on an increasing proportion of model-generated data, performance can degrade due to narrowed coverage and accumulated bias. Existing work mainly studies how to bound this degradation. In iterative model evolution, however, the more meaningful objective is to ensure that each successive model improves over its predecessor, which requires diagnosing collapse at a granularity that is actionable for data curation. We study this problem in synthetic data self-improving for instruction tuning. We show that collapse in this setting is not simply uniform performance ...
| Authors | Xiaonan Luo, Yue Huang, Kehan Guo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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