When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs
Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control. We study this risk in code LLMs, where AI-generated code can enter real repositories, later become training data, and create a repository-scale self-training loop. While software development traditionally interrupts this loop through pull-request review, tests, compilation, and human approval, AI coding tools now produce code faster than humans can review it, and code review itself is increasingly automated by AI systems. We therefore compare three recursive fine-tuning regimes: no review, human-style exogenous-gate review using model-independent...
| Authors | Xinyuan Song, Zekun Cai, Liang Zhao |
|---|---|
| Primary category | cs.SE |
| Published | 26 June 2026 |
| Last updated at source | 12 October 2026 |
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