Fyxer如何建立一个AI的执行助理 人民信任
Fyxer 使用 OpenAI 模型,微调,内存和真实的用户反馈来组织每个用户声音中的收件箱和邮件草稿.
AI NEWS JOURNAL
全部 AI 动态
保留完整资讯流，用来源、主题和时间找到你关心的消息。
Paul Christiano加入OpenAI基金会董事会
保罗·克里斯蒂安诺加入了OpenAI基金会董事会及其安全保障委员会,带来了AI协调,安全和标准方面的经验.
精细调整350M模式,以100 GRPO步骤改进结构产出
精细调整350M模式,以100 GRPO步骤改进结构产出
培训和精细调整多变量嵌入模型,配有变刑器
培训和精细调整多变量嵌入模型,配有变刑器
NVIDIA NeMo AutoModel 加速变速器精细调试
NVIDIA NeMo AutoModel 加速变速器精细调试
超越洛拉:你能击败最流行的微调技术吗?
超越洛拉:你能击败最流行的微调技术吗?
Import AI 461: "Alignment is not on track"; FrontierCode; and synthetic research interns
Where are your agents right now?
译文排队中，当前为原文 · enEvaluating alignment of behavioral dispositions in LLMs
Generative AI
译文排队中，当前为原文 · enRL without TD learning
In this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer. Unlike traditional methods, this algorithm is not based on temporal difference (TD) learning (which has scalability challenges), and scales well to long-horizon tasks. We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning. Problem setting: off-policy RL Our problem setting is off-policy RL. Let’s briefly review what this means. There are two classes of algorithms in RL: on-policy RL and off-policy RL. On-policy RL means we can only use fresh data collected by the current policy. In other words, we have to throw away old data each time we update the policy. Algorithms like PPO and GRPO (and policy gradient meth
译文排队中，当前为原文 · enUnlocking the potential of vision language models on satellite imagery through fine-tuning
Unlocking the potential of vision language models on satellite imagery through fine-tuning
译文排队中，当前为原文 · enMistral微调黑客马拉松
我们很高兴地宣布，Mistral AI微调黑客马拉松将于2024年6月5日至30日以线上形式举行。