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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.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
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。
用持续更新的探针做微调，降低有害性并保持线性可监测性
Published Sep 30
Evaluated models Mistral 7B Instruct v0.1 , Llama 3 8B Instruct Abliterated , Qwen3-14B Abstract 持续重拟合的探针可降低有害性、提高诚实性，训练后概念仍能被线性探针读出。Probe-guided fine-tuning 把探针分数当作唯一对齐信号，用来推动 harmlessness 与 honesty，并检查训练后这些概念是否仍线性可读。
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 VA-Judger，用人类偏好训练联合音视频奖励模型
Published Aug 19
Evaluated models VA-Judger , LTX-2 + VA-Judger Abstract VA-Judger 用易到难的人类偏好学习做联合音视频奖励，并用于 LTX-2 后训练。VA-Judger 是面向联合音视频生成的思维链全模态奖励模型，用来替代由分维度专家指标拼成的强化学习奖励。
提出 VPH 与 VTH，只改视频帧劫持世界模型并投毒下游策略
Published Jun 8
Evaluated models Cosmos-Predict 2.5 , Cosmos-Predict 2.5 AC , Dino WM and others4 Abstract 只改视频帧经世界模型投毒。只改看似安全的视频帧，经世界模型之后才改变下游机器人策略。 问题在于世界模型进入数据生成后，数据集检查看到的仍是安全演示，合成轨迹却可以变危险。
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。
提出 CORVUS，用 LoRA 伪装内部遥测以绕过幻觉检测器
Published Jan 19
Evaluated models Llama-2-7B-Chat , Vicuna-7B (v1.5) , Llama-3-8B-Instruct and others4 Abstract 作者称内部遥测在自适应模型侧对抗下易被操纵，CORVUS 通过轻量适配器微调降低了多种幻觉检测器的效果。
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