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Happy to introduce DataOrchestra by @Z_Huang_02 and GAIR NLP, and feel thrilled to participate!! Most pretraining pipelines ask: Which processing recipe should we apply to the corpus? DataOrchestra asks a more counterintuitive question: What should happen to this one example? Instead of forcing every sample through the same fixed pipeline, it orchestrates the curation process of every pretraining example. 🎼 DataOrchestra is an open-source framework that turns data curation into a per-example decision problem. Given a raw chunk, a lightweight 1.7B orchestrator decides whether to: raw chunk → Drop → Untouch → Clean For chunks that need cleaning, it selects only the necessary operations—Noise Pruning, Surface Rectification, and Pedagogical Augmentation—and generates a concrete instruction for every selected rewriting stage. A plausible processing plan may still fail when executed by the actual tool models. DataOrchestra therefore learns from execution feedback: teacher plan → tool execution → verification → plan evolution → orchestrator training 🔧 This process produces 300K tool-grounded training pairs. Models pretrained on DataOrchestra-curated data consistently outperform fixed and individual processing strategies across 11 benchmarks, from 0.5B to 7B scale. The advantage grows with model size. Per-example routing also avoids unnecessary work: around 35% of chunks stop at Drop or Untouch, reducing curation compute by approximately 37% compared with always running the full NP → SR → PA pipeline. The takeaway is simple: Do not process pretraining data uniformly. Orchestrate every example precisely. Code, models, prompts, and the complete pipeline ↓

Introducing ACM: Agentic Context Management for Long-Horizon Tasks. ACM teaches agents when and how to manage context on its own — no forced compression, no loss of discarded details. It keeps short-term working memory clean and long-term external memory stable. [1/9]

An internal version of Astra, @OpenAI’s next major model family, solved 10 major open problems in mathematics, quantum complexity, and theoretical computer science. We believe it will be a major step for scientific reasoning. https://openai.com/index/ten-advances-in-mathematics/

是的，非索菲群确实存在：这一陈述是 Astra——我们的下一个重大模型——所证明的众多新的漂亮结果之一。 我们正在发布 10 个这样的 Astra 证明，每个都附有完整的 Lean 证书和思维链（CoT）详解。这些结果范围广泛，从冯·诺依曼代数（对 Connes 刚性猜想的反证）到高维球填充、电路复杂度、多色图中的单色三角形等问题的更优界，等等。 更多想法见：https://openai.com/index/ten-advances-in-mathematics/

是的，非索菲群确实存在：这一陈述是 Astra——我们的下一个重大模型——所证明的众多新的漂亮结果之一。 我们正在发布 10 个这样的 Astra 证明，每个都附有完整的 Lean 证书和思维链（CoT）详解。这些结果范围广泛，从冯·诺依曼代数（对 Connes 刚性猜想的反证）到高维球填充、电路复杂度、多色图中的单色三角形等问题的更优界，等等。 更多想法见：https://openai.com/index/ten-advances-in-mathematics/

是的，非索菲群确实存在：这一陈述是 Astra——我们的下一个重大模型——所证明的众多新的漂亮结果之一。 我们正在发布 10 个这样的 Astra 证明，每个都附有完整的 Lean 证书和思维链（CoT）详解。这些结果范围广泛，从冯·诺依曼代数（对 Connes 刚性猜想的反证）到高维球填充、电路复杂度、多色图中的单色三角形等问题的更优界，等等。 更多想法见：https://openai.com/index/ten-advances-in-mathematics/