Google团队推出EnvHarness，能让静态环境自适应智能体训练，自动诊断并修复策略缺陷，效率提升近10%。
Google Cloud AI Research联合华盛顿大学和UNC Chapel Hill发布EnvHarness，这是一个Apache-2.0许可的可编程层。该层将静态智能体基准测试转变为适应策略训练的环境，通过reset()/step()接口包装冻结环境。在五个基准测试中，挖掘的技能在保留任务上最多提升9.0分，执行步骤减少9.8%。
Google AI Introduces EnvHarness: A Programmable Layer That Turns Static Agent Environments Into Adaptive Training Worlds
Google Cloud AI Research, with Washington University in St. Louis and UNC Chapel Hill, has released EnvHarness, an Apache-2.0 layer that turns a static agent benchmark into one that adapts to the policy training on it. It wraps a frozen environment through the standard reset()/step() interface, so tasks and human-built verifiers stay untouched — and an LLM designer, EnvRigger, writes those wrappers automatically against flaws diagnosed in the agent's own rollouts. Across five benchmarks, mined skills gain up to 9.0 points on held-out tasks with 9.8% fewer execution steps. The post Google AI Introduces EnvHarness: A Programmable Layer That Turns Static Agent Environments Into Adaptive Training Worlds appeared first on MarkTechPost .