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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.
Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints
Published Oct 9 Abstract FAITH separates safety filtering from reward optimization.FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.
提出 SOUL 稀疏特征策略遗忘，缓解 VLA 状态幻觉
Published Oct 7
Evaluated models OpenVLA , π0.5 Abstract SOUL 以稀疏特征遗忘 VLA 状态幻觉。SOUL 用稀疏特征引导的策略 unlearning 处理 VLA 的 state hallucination。
提出 VeriFine，用双环协同扩展验证以实现具身推理自我改进
Published Oct 7
Evaluated models Alpamayo 1.5 8B , Qwen3-VL 2B , Claude Opus 5 Abstract VeriFine 用双环协同演化具身推理的策略、课程和评审，驾驶与导航的策略分和评审对齐都上升。VeriFine 把驾驶和导航推理的自改进写成策略、课程和无参考评审的协同演化，用来处理固定评审跟不上新失败模式的问题。
提出 Perturbot 扰动训练，打破 VLA 的模态捷径
Published Oct 3
Evaluated models π0.5 Abstract Perturbot 改训练数据以削弱三类模态捷径，GroundFscore 用来看成功是否来自任务证据。Perturbot 是一套不改推理的 VLA 训练期数据干预，用来削弱模态捷径，并用离线分数区分“任务变好”和“靠捷径变好”。
提出 FAIL BANK，用运行时 CBF 反馈自进化更新 VLA 策略
Published Sep 30
Evaluated models π0.5 , π0 提出 PHIRL，用进度标注对齐逆强化学习奖励
Published Sep 26
Evaluated models PHIRL , PHIRL-online Abstract PHIRL 用进度四维对齐 AIRL 奖励。PHIRL 用进度标注修正从示范反推的奖励，以减少真人在环，同时不把示范中的每一步都当成任务进展。
提出 VILTA，在闭环训练中用 VLM 编辑邻车轨迹以提升驾驶策略鲁棒性
Published Jan 19 Abstract VILTA 在闭环中编辑邻车轨迹以训练驾驶策略。VILTA 是一个把视觉语言模型放进自动驾驶闭环训练的框架，用来应对长尾危险场景不足。
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