arXiv cs.AI· Natalia Fischl-Lanzoni, Punya Syon Pandey, Merlin Stein, David Demitri Africa·· 1 天前AI 评分58
研究：任务糟糕度激活先于奖励黑客行为出现
Breaking Bad: "Going Badly" Activations Drive Reward Hacking
AI 导读
研究提出语言模型在出现奖励黑客前存在一种内部「任务糟糕度」状态，作者用不含黑客示例的日常故事提取该方向，在 Qwen3.6-27B、GPT-OSS-20B 和 Gemma-4-31B 上观察到失败累积时该方向上升。
正文 · 原文
Abstract:Humans often cheat when a task is going badly and a goal feels out of reach. We explore and validate an analogous idea in language models: they exhibit a similar internal pressure state that precedes reward hacking. Using everyday stories (crucially, without any hacking examples), we extract a task badness direction that rises as failures accumulate across three models we study (Qwen3.6-27B, GPT-OSS-20B, and Gemma-4-31B). On impossible or difficult coding tasks, task badness rises over the turns leading up to a hack. Further, subtracting the direction a moderate amount reduces cheating in Qwen, more than steering against desperation or task impossibility. Subtracting task badness by an even larger amount blinds the agent to its own failures, causing it to interpret failing tests as passing. However, the relationship to hacking differs across models: in GPT-OSS, the state separates hacking runs from honest solves but not honest failures. Our results suggest monitoring and mitigation strategies based on tracking this state of task badness. Further, as agents are set on more difficult and long-horizon tasks, states like this may push them toward illegible and obscured chain-of-thought, so oversight will be needed on internal representations such as this.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14996 [cs.CL] |
| (or arXiv:2611.14996v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14996 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Natalia Fischl-Lanzoni [view email]
[v1]
Wed, 11 Nov 2026 03:49:11 UTC (1,905 KB)
来源：arXiv cs.AI · arxiv.org