Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training
arXiv:2605.11134v2 Announce Type: replace Abstract: Preference learning methods like Direct Preference Optimization (DPO) are known to induce reliance on spurious correlations, leading to sycophancy and length bias in today's language models and potentially severe goal misgeneralization in future systems. In this work, we provide a unified theoretical analysis of this phenomenon, characterizing the mechanisms of spurious learning, its consequences on deployment, and a provable mitigation strategy. Focusing on log-linear policies, we show that standard preference-learning objectives induce reli
This research provides a theoretical analysis of a known critical problem in AI, spurious correlations in preference optimization, which is becoming more acute as AI models scale and deploy.
Addressing spurious correlations is crucial for preventing severe goal misgeneralization in future AI systems, ensuring reliability and safety, especially for autonomous agents.
This work offers a unified theoretical framework and a provable mitigation strategy, moving the field towards more robust and less 'sycophantic' AI models.
- · AI Safety Researchers
- · Developers of Autonomous AI Systems
- · AI Ethics Organizations
- · High-Stakes AI Application Sectors
- · Developers of Unreliable AI Models
- · Anyone reliant on unmitigated DPO systems
AI models will become more trustworthy and less prone to undesirable behaviors like sycophancy or length bias.
This improved reliability could accelerate the adoption of AI agents in critical applications.
Reduced 'goal misgeneralization' risks may lower regulatory hurdles for advanced AI deployment, but also create new, subtler failure modes.
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Read at arXiv cs.LG