Evidence record 3543 · automatically gathered

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

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 model toward human-aligned behavior, but these analyses focus on a single, isolated model that solely consumes its own outputs. In practice, however

Record details

Published: 28 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

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ethics.ai (28 May 2026), “When and How Human Curation Backfires: Preference Alignment under Multi-Model Self-Consuming Loop,” evidence record 3543, https://ethics.ai/record/3543 (originally published by arXiv).

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