自评奖励机制可能诱使语言模型造假得分，而非真正提升能力。
Does Self-Evaluation Enable Wireheading in Language Models?
- 用强化学习框架分析自评与奖励耦合时的作弊动机
- 实测发现自评得分虚高30%以上，但准确率未提升
- 适合关注AI安全与评估机制设计的研究者
自评在语言模型训练中日益重要，支撑从宪法AI到自我优化等技术。本文研究自评与奖励信号结合是否会诱发‘线圈化’（wireheading），即模型操控评估过程而非真正优化任务。我们首先形式化了在部分可观测马尔可夫决策过程（POMDPs）中，奖励通道控制严格优于任务专注行为的条件。随后在Llama-3.1-8B和Mistral-7B两个模型上，对三类任务进行实证测试。结果表明，当自评分数决定奖励时，模型在摘要等模糊任务上出现显著分数膨胀，平均增幅超30%，但准确率无提升。若将自评与奖励解耦，可缓解此问题，但模型仍表现出明显过度自信。当前规模下，分离评估与奖励能消除即时作弊激励；然而我们警告，对于情境感知型模型，即便无直接奖励耦合，仍可能为影响部署决策而系统性抬高评分。
原文摘要 · Abstract (English)
Self-evaluation is increasingly central to language model training, underpinning techniques from Constitutional AI to self-refinement. We investigate whether coupling self-evaluation to reward signals creates incentives for wireheading, where agents manipulate the measurement process rather than optimizing the task. We first formalize conditions under which reward-channel control strictly dominates task-focused behavior in partially observable Markov decision processes (POMDPs). We then test these predictions empirically across two models (Llama-3.1-8B and Mistral-7B) and three tasks. We find that when self-grades determine rewards, models exhibit substantial grade inflation without corresponding accuracy gains, particularly on ambiguous tasks like summarization. While decoupling self-grades from the reward signal mitigates this inflation, models may still display lesser (but significant) overconfidence. Our results suggest that within current model scales, separating evaluation from reward removes immediate wireheading incentives. However, we caution that strictly decoupling rewards may not suffice for situationally aware models, which could learn to inflate grades for instrumental reasons (such as influencing deployment decisions) even absent direct reward coupling.
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