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Oct 12 · Monday
Another 313 papers do not have category tags yet and are not included in filtered results.
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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.
比较连续后训练阶段如何改变语言模型的说服力
Published Oct 7
Evaluated models mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated , GPT-4 Abstract 作者称说服性 SFT 提高失调模型的态度变化，IPO 无额外收益，GPT-4 不异于中性文本。作者用同一人类协议，把 LLM 说服力拆成阴谋数据上的失调 SFT、说服数据上的 SFT，以及其后的 IPO。
评估自动化 AI 研发触发智能爆炸的可能并提出政策应对
Published Sep 29 Abstract 软件驱动智能爆炸可能把数年进展压进数月，作者要求三项政策先做好。Chan 等论证：自动化 AI 研发可能经软件反馈环形成智能爆炸，把本需数年的进展压进数月或更短。他们关心的不是换硬件的建设周期，而是 AI 扩大有效研发劳动力、再产出更强系统，以及社会能否在能力跃迁前看见、约束并适应这一过程。
提出 OpenMLE，后训练元进化智能体并做机器学习工程长程搜索
Published Jul 31
Evaluated models Frontis-MA1-35B , Frontis-MA1-30B Abstract OpenMLE 训练 Frontis-MA1-35B，用四个共享算子把 MLE 后训练和长程搜索接成元进化闭环。作者提出全栈 OpenMLE ，在机器学习工程里研究从进化到元进化的一步，并把递归自我改进视为更远目标。智能体要在延迟、有噪声的执行反馈下反复修改程序。作者认为公开系统很少同时提供可扩展环境、基于执行的后训练，以及部署该模型的长程进化搜索。
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