Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning
Quick Answer
The paper addresses model collapse in synthetic data learning for iterative instruction tuning, proposing KITE, a two-stage framework that enhances stability in model performance.
Quick Take
KITE combines failure-guided data generation with boundary-aware uncertainty curation, outperforming existing synthetic data baselines across various datasets and open-source .
Key Points
- Model collapse leads to performance degradation in synthetic data learning.
- KITE framework improves model stability by addressing competence polarization.
- Experiments show KITE outperforms strong synthetic-data baselines.
- The approach focuses on actionable data curation for iterative model evolution.
- KITE combines two stages: failure-guided generation and uncertainty curation.
DeepSignal Analysis
What happened
The paper discusses the issue of model collapse in synthetic data learning for iterative instruction tuning, introducing a framework called KITE. This framework aims to enhance model performance stability by combining failure-guided data generation with boundary-aware uncertainty curation. KITE reportedly outperforms existing synthetic data methods across various datasets and open-source large language models (LLMs).
Key evidence
- The paper identifies model collapse as a significant challenge when training large language models on synthetic data, leading to performance degradation.
- KITE is proposed as a two-stage framework that integrates failure-guided data generation with boundary-aware uncertainty curation to address model collapse.
- Experiments demonstrate that KITE provides more stable improvements compared to existing synthetic data baselines across multiple datasets and open-source LLMs.
Why it matters
Understanding and addressing model collapse is crucial for the development of robust AI systems, particularly in iterative instruction tuning. The proposed KITE framework could lead to more reliable performance in LLMs by mitigating the risks associated with training on synthetic data. This advancement may enhance the overall effectiveness of AI applications that rely on synthetic data for training, potentially leading to better user experiences and outcomes.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 degradation, but can appear as a polarization of competence, where synthetic training reinforces already strong skills while further degrading weak ones. Motivated by this observation, we propose KITE (Knowledge-boundary Instruction Tuning via Exploration), a two-stage framework that combines failure-guided data generation with boundary-aware uncertainty curation. Experiments across several datasets and multiple open-source LLMs show that KITE yields more stable improvement than strong synthetic-data baselines.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.17043 [cs.CL] |
| (or arXiv:2607.17043v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.17043 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xiaonan Luo [view email]
[v1]
Sun, 19 Jul 2026 03:20:01 UTC (426 KB)
— Originally published at arxiv.org
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