arXiv — NLP / Computation & Language · · 3 min read

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

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Computer Science > Computation and Language

arXiv:2607.17043 (cs)
[Submitted on 19 Jul 2026]

Title:Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

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Abstract: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)
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