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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.
提出 LineupRL，用描述到序列识别作可验证奖励训练时序描述模型
Published Oct 1 Abstract LineupRL 用统计近邻上的序列识别做可验证奖励，3B 描述器超过 SFT、两种 RL 奖励和 72B 教师。LineupRL 是给开放式时间序列描述用的 RLVR：不模仿合成参考，而奖励一条描述能否让冻结文本 LLM 从相近序列里认出目标。
B IAS R EDUCER 只改奖励头，按数据集选择属性编辑。
Published Sep 26
Evaluated models Skywork-Reward-V2-Qwen3-1.7B , GRM-Llama3.2-3B-rewardmodel-ft , RM-Mistral-7B and others7 Abstract BIASREDUCER 按数据集选择奖励头编辑，五模型三基准平均提升并迁移到下游训练。BIASREDUCER 是一种只改线性奖励头的奖励模型编辑框架，用来降低模型对预定义表面属性的依赖，并按新数据集决定改哪些属性。
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