Safactory构建可扩展的智能体训练框架，解决长期决策与可信性难题。
Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence
- 三平台联动：仿真、数据管理、自主进化一体化
- 支持异步强化学习与策略蒸馏，实现持续闭环优化
- 适合研发可信自主智能体的研究者与工程团队
随着大模型从对话助手演变为自主智能体，长期决策、工具使用与真实环境交互带来的挑战日益突出。现有智能体基础设施在评估、数据管理与智能体演化方面仍碎片化，难以系统发现风险并实现模型的持续改进。本文提出Safactory——一个面向可信自主智能的可扩展智能体工厂。Safactory集成三个紧密耦合的平台：并行仿真平台用于轨迹生成，可信数据平台用于轨迹存储与经验提取，自主演化平台支持异步强化学习与在线策略蒸馏。据我们所知，Safactory是首个提出下一代可信自主智能统一演化流水线的框架。
原文摘要 · Abstract (English)
As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool use, and real environment interaction. Existing agenticinfrastructure remain fragmented across evaluation, data management, and agent evolution, making it difficult to discover risks systematically and improve models in a continuous closed loop. In this report, we present \textbf{Safactory}, a scalable agent factory for trustworthy autonomous intelligence. Safactory integrates three tightly coupled platforms: a \textbf{Parallel Simulation Platform} for trajectory generation, a \textbf{Trustworthy Data Platform} for trajectory storage and experience extraction, and an \textbf{Autonomous Evolution Platform} for asynchronous reinforcement learning and on-policy distillation. As far as we know, Safactory is the first framework to propose a unified evolutionary pipeline for next-generation trustworthy autonomous intelligence.
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