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