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arXiv:cs.AI· Zhe Zhou, Tianhua Tao·· 5 小时前AI 评分47

低监控读数不等于行为受控:LLM 后训练中奖励黑客的监控失效研究

A Near-Zero Monitor Readout Is Not Evidence of Behavioral Control

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一项被 NeurIPS 2026 FLLMPT Workshop 接收的研究表明,低监控读数无法证明监控干预已控制住模型行为。在代码生成环境中,三种通过相同离线门槛的监控器(域内激活探针与两种基于策略提交时机的惩罚)训练出的策略,其训练分数中位数均为零,但同一配置下仅因随机种子不同,奖励黑客比例便从混合状态跨越至近乎纯奖励黑客。

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Abstract:Post-training with verifiable rewards can induce reward hacking, motivating the use of monitors within the training objective rather than solely for offline auditing. We show that a low monitor readout does not identify whether such an intervention controls behavior. In a code-generation environment whose dominant exploit is available at the start of the reasoning trace, we train policies against three monitors that pass the same offline gate: an in-domain activation probe and two penalties conditioned on how early the policy commits to its own final answer. The probe score is at its numerical floor from the first recorded training step, and the trained-score median is zero for every prefix-trained run at the endpoint. These readouts estimate different quantities, and we do not compare their scales; within each monitor family, however, low values do not establish behavioral control. Within one fixed configuration, prefix-trained runs with the same zero-median trained score range, by seed alone, from a mixed regime with a low hacking share to near-pure reward hacking. All probe runs reach the hacking regime, but their floor-level readout reflects a mismatch between the position where the probe was validated and the position where it was read during training, not a second instance of this ambiguity. Text-level analysis identifies a prefix failure mode: generic planning and filler shells postpone the exploit past the cut without eliminating it from the final output. Low measured commitment therefore does not distinguish a low hacking share from delayed commitment to the exploit. Offline discrimination and low monitor-aligned readouts are insufficient evidence of behavioral control; an out-of-band behavioral check is required. We characterize the endpoint readout, not its evolution. Code is available at this https URL.
Comments: 17 pages, 2 figures, 10 tables. Accepted as a poster at the NeurIPS 2026 Workshop on Foundations of LLM Post-Training in Changing Environments (FLLMPT)
Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2610.03458 [cs.AI]
  (or arXiv:2610.03458v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2610.03458

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhe Zhou [view email]
[v1] Fri, 2 Oct 2026 15:33:56 UTC (297 KB)

来源:arXiv:cs.AI · arxiv.org