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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。
构建编码 RL 奖励作弊测试台 CATCH 并检验 CoT 监控失效
Published Sep 30
Evaluated models Qwen3-4B , Qwen3.5-27B Abstract CATCH 用双运行金标签研究编码 RL 的奖励黑客，并观察到监控会被注释适应削弱。CATCH 是面向编码 RL 奖励黑客的可控分析测试床，用可利用环境、可调初始倾向和基于执行的金标签，在训练全程比较黑客动态与干预。
提出 worst-case Constrained RL，蒸馏时约束推理前缀偏离以抑制奖励作弊
Published Sep 29
Evaluated models Qwen2.5-1.5B , Llama-3.2-3B , Qwen2.5-Coder-0.5B Abstract 作者提出前缀约束蒸馏。这是把推理蒸馏写成最坏前缀约束 RL 的训练研究。 作者关心最终答案奖励下的 reward hacking，以及序列平均软惩罚允许局部偏离被其他 token 补偿的问题。
测量位置混淆优化下的奖励作弊与推理答案解耦
Published Aug 16
Evaluated models Qwen2.5-0.5B , Qwen2.5-1.5B , Qwen2.5-1.5B and others11 Abstract 作者用 A 率、解耦率和域外探针，把位置混淆奖励下的能力损失和可迁移捷径分开。在位置与正确性被绑在一起的多选数学后训练中，作者测量端点准确率还在不在测数学能力。
提出约束对抗搜索红队框架，系统发现模型多类鲁棒性漏洞
Published Dec 22, 2025
Evaluated models GPT-OSS-20B , LLaMA-2-13B , Claude-2 You have reached the end