Google提出EnvHarness：动态重塑环境以强化Agent训练
Another great Google paper.
Agent训练中的环境自适应是提升泛化能力的关键方向，这篇论文给出了具体的自动化实现方案与实测数据，对做Agent的同学有参考价值。
Another great Google paper.
Agent training has a ceiling: the agent improves, but the same environment stays frozen.
EnvHarness makes the environment adapt too, without rebuilding the benchmark or its verifier.
EnvHarness does this by reshaping an existing environment around whatever the agent is currently bad at, while keeping the original task and verifier intact.
If a coding agent skips tests, for example, the environment can reject submission until tests run; if it relies on a shortcut, that shortcut can be blocked.
EnvRigger finds these weaknesses from rollouts, writes the wrapper, then keeps it only if fresh runs show the new setup is both useful and solvable.
On SWE-bench Verified, with the same 300-environment budget, agents resolved 54.79% of issues, versus 52.13% with original environments and 50.37% with generated ones.
– arxiv. org/abs/2608.19880
Title: "EnvHarness: Awakening Static Worlds for Agent Learning"
更进一步：量化金融体系
看懂新闻只是起点——沿量化金融路径，把它变成能交付的工程能力