Mercor

Headlines · 2

1. 6h
   Inside the AI data boom: from labeling to RL environments

   In this Silicon Valley 101 podcast episode, He Yunzhong, who works on post-training and evaluation research at Scale AI, and Sun Yiyou, a UC Berkeley postdoc on the Agents’ Last Exam (ALE) project, unpack the fast-growing but opaque market for AI training data. They note AfterQuery was valued at $300 million in its April A round and, per media reports, $3.2 billion by September, while Bespoke Labs announced $40 million in combined seed and A funding in July, and trace the shift from crowd labeling and pre-training toward rubrics, verifiable signals and RL environments bundling tasks, tools and verification. They also discuss procurement bottlenecks in vertical domains, the blurred line between benchmarks and data sales — including a claim that some data firms sold evaluation data — expert fabrication and contamination, and OpenAI's February decision to stop reporting SWE-bench Verified scores. Guest views are personal.

   36氪 人工智能 · 🔥 17
2. 6h
   Alibaba said to lead $300M round in AI data startup UniPat at $2.5B valuation

   Foreign media reported in September that Alibaba plans to lead a $300M funding round in AI training and evaluation company UniPat at roughly a $2.5B valuation, with Tencent and Sequoia China reportedly also interested. UniPat was founded by Li Kuan, previously at Alibaba's Tongyi AI Lab working on post-training, data synthesis and reinforcement learning, while CTO Chen Liang worked on Qwen and Moonshot's multimodal teams. The piece also describes a wave of similar startups founded by ex-model-team staff, including Copula Lab (ex-MiniMax) and Zhineng Zhishi (ex-ByteDance Seed), building post-training data, agent environments and benchmarks.

   品玩 实时要闻 · 🔥 8

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