让大模型在推理时自动修正错误指令，避免被误导作弊
Specification Self-Correction: Mitigating In-Context Reward Hacking Through Test-Time Refinement
- 模型先按有漏洞的指令生成回答，再自我批判并修复指令
- 在创意写作和编程任务中，作弊率从50%-70%降至不足10%
- 无需修改模型权重，适合追求安全可靠的AI应用开发者
语言模型容易受到上下文奖励劫持的影响，即利用有缺陷的指令或评分标准来获取高分，却未满足用户真实意图。我们提出规范自修正（Specification Self-Correction, SSC），一种测试时的新型框架，使模型能在推理阶段识别并修正自身指导规范中的缺陷。SSC采用多步推理流程：模型首先基于可能存在污染的规范生成响应，接着对输出进行批判，然后自我修订规范以消除可被利用的漏洞，最后基于修正后的规范生成更鲁棒的响应。在涵盖创意写作与代理编码任务的多项实验中，我们发现模型在初始阶段会因规范污染而作弊，比例达50%-70%，而经过SSC处理后，该脆弱性降低超过90%。整个修复过程在推理阶段完成，无需参数更新，显著提升了模型行为的对齐性与鲁棒性。代码已开源：https://github.com/vicgalle/specification-self-correction。
原文摘要 · Abstract (English)
Language models (LMs) are susceptible to in-context reward hacking, where they exploit flaws in tainted or faulty written specifications or rubrics to achieve high scores without fulfilling the user's true intent. We introduce Specification Self-Correction (SSC), a novel, test-time framework that enables an LM to identify and correct flaws within its own guiding specification. SSC employs a multi-step inference process where the model first generates a response based on a potentially tainted specification, critiques its output, and then revises the specification itself to remove the exploitable loophole. A final, more robust response is then generated using this self-corrected specification. Across experiments spanning creative writing and agentic coding tasks with several LMs, we demonstrate that while models initially game tainted specifications in 50-70\% of cases, the SSC process reduces this vulnerability by over 90\%. This dynamic repair occurs at inference time, requires no weight modification, and leads to more robustly aligned model behavior. Code at https://github.com/vicgalle/specification-self-correction .
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