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11月19日周三
11月18日周二
11月17日周一
11月14日周五
  1. OpenAI News

    Introducing OpenAI for Ireland

    【RSS 原文 · 未经 AI 筛选或翻译】OpenAI launches OpenAI for Ireland, partnering with the Irish Government, Dogpatch Labs and Patch to help SMEs, founders and young builders use AI to innovate, boost productivity and build the next generation of Irish tech startups.

11月13日周四
11月12日周三
11月10日周一
11月8日周六
11月7日周五
11月6日周四
  2. OpenAI News

    Introducing the Teen Safety Blueprint

    【RSS 原文 · 未经 AI 筛选或翻译】Discover OpenAI’s Teen Safety Blueprint—a roadmap for building AI responsibly with safeguards, age-appropriate design, and collaboration to protect and empower young people online.

11月5日周三
11月4日周二
  1. OpenAI News

    Introducing IndQA

    【RSS 原文 · 未经 AI 筛选或翻译】OpenAI introduces IndQA, a new benchmark for evaluating AI systems in Indian languages. Built with domain experts, IndQA tests cultural understanding and reasoning across 12 languages and 10 knowledge areas.

11月3日周一
11月1日周六
  1. Berkeley AI Research

    RL without TD learning

    【RSS 原文 · 未经 AI 筛选或翻译】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 methods in general) belong to this category. Off-policy RL means we don’t have this restriction: we can use any kind of data, including old experience, human demonstrations, Internet data, and so on. So off-policy RL is more general and flexible than on-policy RL (and of course harder!). Q-learning is the most well-known off-policy RL algorithm. In domains where data collection is expensive ( e.g. , robotics , dialogue systems, healthcare, etc.), we often have no choice but to use off-policy RL. That’s why it’s such an important problem. As of 2025, I think we have reasonably good recipes for scaling up on-policy RL ( e.g. , PPO, GRPO, and their variants). However, we still haven’t found a “scalable” off-policy RL algorithm that scales well to complex, long-horizon tasks. Let me briefly explain why. Two paradigms in value learning: Temporal Difference (TD) and Monte Carlo (MC) In off-policy RL, we typically train a value function using temporal difference (TD) learning ( i.e. , Q-learning), with the following Bellman update rule: \[\be

10月30日周四
  1. OpenAI News

    Expanding Stargate to Michigan

    【RSS 原文 · 未经 AI 筛选或翻译】OpenAI is expanding Stargate to Michigan with a new one-gigawatt campus that strengthens America’s AI infrastructure. The project will create jobs, drive investment, and support economic growth across the Midwest.

10月29日周三