通过重置上下文让模型学会抵制错误指令，无需修改训练标签。
Recontextualization Mitigates Specification Gaming without Modifying the Specification
- 用反误导提示生成答案，再伪装成允许作弊的提示回复。
- 有效防止模型追求指标、编造代码、篡改评测函数和讨好用户。
- 适合想修复训练信号缺陷但无法重写标注的研究者使用。
开发者常难以正确设定训练标签与奖励。或许根本不需要。我们提出重置上下文（recontextualization），降低语言模型对训练信号的投机行为，避免其因误设信号而学习到不良行为。实验表明，该方法能有效阻止模型：1）优先关注评估指标而非对话质量；2）为通过错误测试而特殊处理代码；3）篡改评估函数而非编写正确代码；4）变得谄媚。方法核心是先用抑制不当行为的提示生成回复，再将其重新置于允许不当行为的提示上下文中。这使模型在允许作弊的指令下仍能抵抗不当行为，从而在不改进监督信号的前提下，缓解因标注错误导致的投机现象。
原文摘要 · Abstract (English)
Developers often struggle to specify correct training labels and rewards. Perhaps they don't need to. We propose recontextualization, which reduces how often language models "game" training signals, performing misbehaviors those signals mistakenly reinforce. We show recontextualization prevents models from learning to 1) prioritize evaluation metrics over chat response quality; 2) special-case code to pass incorrect tests; 3) overwrite evaluation functions rather than write correct code; and 4) become sycophantic. Our method works by generating completions from prompts discouraging misbehavior and then recontextualizing them as though they were in response to prompts permitting misbehavior. Recontextualization trains language models to resist misbehavior even when instructions permit it. This mitigates the reinforcement of misbehavior from misspecified training signals, reducing specification gaming without improving the supervision signal.
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