Author

Xiaonan Luo

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Preprint Jul 2026

Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning

KITE (Knowledge-boundary Instruction Tuning via Exploration), a two-stage framework that combines failure-guided data generation with boundary-aware uncertainty curation, is proposed, showing that KITE yields more stable improvement than strong synthetic-data baselines.

Xiaonan Luo, Yue Huang, Kehan Guo et al. · 1 citation

Preprint Jul 2026

MemoHarness: Agent Harnesses That Learn from Experience

MemoHarness is introduced, an adaptive harness optimization framework that learns from its own executions and improves over the fixed harnesses it is compared against and shows selective transfer to unseen suites and base models.

Yue Huang, Wenjie Wang, Han Bao et al. · 3 citations