When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift
arXiv:2605.25629v1 Announce Type: new Abstract: Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched train--test distributions. Therefore, we study W2S preference learning under zero-shot distribution shift and find that strong students trained on weak preference labels can appear successful in-distribution while failing to transfer across preference datasets. We provide evidence for a representational failure mode in which weak-supervised fine-tuning can pull the strong model toward source-domain featur
This research addresses a critical limitation in weak-to-strong generalization methodologies, which is becoming more apparent as AI scales, particularly in preference learning for complex tasks.
A strategic reader should care because this highlights a fundamental hurdle in AI oversight—ensuring robust and transferable alignment even when supervision is weak, impacting the reliability and safety of advanced AI systems.
The understanding that W2S models can fail under distribution shift means current evaluation methods may overstate AI alignment capabilities, requiring more rigorous testing regimes and possibly new architectural approaches.
- · AI safety researchers
- · Robust AI evaluation platforms
- · Developers of transfer learning techniques
- · Developers relying solely on in-distribution W2S evaluation
- · Systems with high-stakes deployment without robust out-of-distribution testing
It becomes evident that current weak-to-strong generalization methods may produce AI models that perform unreliably when exposed to novel inputs or scenarios.
This necessitates a significant investment in developing AI systems that can reliably generalize across diverse data distributions, moving beyond purely in-distribution success metrics.
The development of truly aligned and robust AI, capable of operating safely in unpredictable real-world environments, may be delayed until these generalization challenges are overcome.
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