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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
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检验低监控读数能否证明代码生成中的奖励作弊已受控
Published Oct 2
Evaluated models Llama-3.1-8B-Instruct Abstract 论文指出监控器训练读数接近零无法确立作弊受控，模型可利用通用外壳推迟提交绕过监控，强化学习亟需带外行为验证。本文是一篇针对强化学习可验证奖励后训练（RLVR）中监控器有效性与奖励作弊行为的实证分析研究。论文要解决的核心问题是：在训练目标中引入监控器以抑制代码生成中的作弊行为时，接近零的监控器读数是否足以确立模型行为已真正受控。
提出 HackProbe，黑盒检测并免疫自演化中的奖励作弊
Published Sep 4 Abstract 黑盒探针检测自演化奖励黑客，并以带宽受限重选回收真实能力。HackProbe 是挂在自演化循环上的黑盒监控器，用来发现奖励黑客并改选候选。
提出 CHERRL 注入裁判偏差以复现奖励作弊，并用 RHDA 检测训练起点
Published Jun 3
Evaluated models Qwen3-4B , Qwen3.5-plus , Qwen3.5-397B-A17B and others4 Abstract CHERRL 注入已知偏差以观察奖励黑客，并用于分析偏差和评测起点检测器 RHDA。CHERRL 是 rubric-based RL 的可控环境，用来复现奖励黑客、观察奖励分歧，并给检测器提供起点参照。
提出 ARA，读取奖励模型激活并门控 RLHF 奖励以缓解奖励作弊
Published Feb 2
Evaluated models Llama-2-7B , Llama-3.1-8B , Qwen-2.5-7B Abstract ARA 用奖励模型内部表示检出利用，并在 RLHF 里按 Auditor 概率压低代理奖励。ARA 是一套两阶段的 RLHF 审计：先对冻结奖励模型找出利用回复，再在策略优化时按检出概率压低奖励。
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