POSEIDON 从科研代码仓库自动构建 9.7 万个智能体训练环境
POSEIDON: Scaling Training Environments for Scientific Agents from Code Repositories
训练科研智能体最缺的就是能跑、能验证的环境，这套流水线从代码仓库自动造出近 10 万个，还把 Qwen3.5 训出了跨基准提升。
POSEIDON 是一条自动化流水线，能把科研代码仓库转换成可执行、可验证的智能体训练环境。它将科研工作流拆解为 Reproduction、Subflow、Completion、Correction 四类任务，同等仓库预算下比此前方法多提取近 10 倍环境。用它构建的 Pearl 数据集包含 97,659 个训练环境，来自 7,110 个公开仓库，覆盖 10 个科学学科。在 Pearl 上训练的 Qwen3.5 模型（9B、27B、35B-A3B）在六个科学智能体基准上均有一致提升。
POSEIDON: Scaling Training Environments for Scientific Agents from Code Repositories
Training environments remain a major bottleneck for agents in data-driven scientific discovery. Existing collections of executable and verifiable training environments are small, and their construction remains difficult to scale. To address this limitation, we introduce Poseidon, an automated pipeline for generating diverse, verifiable training environments from scientific code repositories at scale. From the same repository budget, Poseidon extracts nearly 10 times as many training environments as a prior approach by decomposing scientific workflows into four complementary task types: Reproduction, Subflow, Completion, and Correction, where each type targets a distinct capability for scientific research. A recursive improvement loop further refines failed candidates and recovers otherwise discarded environments. Using Poseidon, we construct Pearl, a collection of 97,659 executable and verifiable environments from 7,110 public repositories spanning 10 scientific disciplines. To the best of our knowledge, Pearl is the largest and most broadly multidisciplinary collection of scientific-agent training environments to date. Qwen3.5 models (9B, 27B, and 35B-A3B) trained on Pearl achieve consistent gains across six challenging scientific-agent benchmarks. Overall, the scale and diversity of Pearl offer opportunities for training scientific discovery agents.