Google新研究EnvRigger和EnvHarness，解决智能体训练环境问题，效果显著，值得一试。
Google提出EnvHarness和EnvRigger，解决智能体训练环境过时问题。EnvHarness通过插件层重塑环境行为，EnvRigger诊断政策缺陷并验证。在四个领域的五个基准测试中，EnvRigger在保留实例上提升了9.0分，执行步骤减少9.8%。
Impressive research from Google on building better environments for agents. Training environments f...
Impressive research from Google on building better environments for agents. Training environments for agents are hand-built and go stale. The agent improves, the environment does not, and it's not able to see the agent's weaknesses in the first place. EnvHarness wraps a static environment in a programmable plug-in layer that reshapes its behavior without touching the underlying logic. Every reshaped environment keeps its original verifier; this is what makes the reshaping safe to train on. EnvRigger treats the policy as a black box, reads its execution trajectories, synthesizes harness components aimed at the diagnosed flaws, then validates them with fresh rollouts. Across five benchmarks in four domains, up to 9.0 points better on held-out instances with 9.8% fewer execution steps. Paper: arxiv.org/abs/2608.19880 Track more trending AI papers in our academy: academy.dair.ai 💬 14 🔄 22 ❤️ 169 👀 11633 📊 61 ⚡