When and How Human Curation Backfires: Preference Alignment under Multi-Model Self-Consuming Loop 事件

When and How Human Curation Backfires: Preference Alignment under Multi-Model Self-Consuming Loop arXiv:2605.29267v1 Announce Type: new Abstract: Foundation models are increasingly trained on synthetic data generated by prior model iterations rather than exclusively on real data. This self-consuming training paradigm can lead to model collapse, divergence, or bias amplification. Recent work (Ferbach et al., 2024) shows that incorporating human curation into the loop can steer a self-consuming m