大模型九成步骤是浪费，只挑关键几步上云
现在让 AI 干一件长活(比如修代码、操作终端)，每一步都调用云端大模型，贵且慢。这篇发现：大部分步骤根本不需要那么强的模型，只有少数关键节点才值得动用大模型。他们做了个叫 EdgeWise 的系统：本地放一个小模型当主力，用强化学习训练它自己判断“哪一步我搞不定、该交给云端”，结果在修代码和终端操作两类任务上，性能不输全程用大模型，推理成本最高降了 62%。它不是你明天就能装上的东西，但指向一个明确趋势：AI 的聪明不该按次计费，而该按需分配。
📄 原文摘要(英文)
Complex and long-horizon agent tasks often rely on frontier models deployed in the cloud to achieve strong performance. However, using these models for every step of a long-horizon trajectory incurs substantial inference cost, even though not all steps may require the same level of model capability. In this paper, we find that allocating the LLM to only a small subset of key steps can achieve performance comparable to full LLM execution while significantly reducing inference cost. Motivated by this observation, we propose EdgeWise, an edge-cloud collaboration framework that deploys an SLM on the local device as the primary solver and selectively delegates critical steps to a cloud LLM when stronger model capacity is needed. To enable autonomous delegation without additional models at inference time, we introduce a step-level RL method that jointly optimizes task solving and delegation, with fine-grained credit assignment for individual delegation decisions. We also introduce an external judge-based delegation strategy and use it to systematically investigate key design choices in edge-cloud collaboration, including when to delegate, how delegation decisions should be made, what context should be shared, and how frequently the cloud LLM should be invoked. Experiments show that EdgeWise matches or outperforms pure-LLM execution while reducing inference cost up to 62% on SWE-Rebench and 34% on Terminal-Bench 2.1. We further deploy EdgeWise on a local workstation, demonstrating comparable cost savings in a practical edge-cloud setting.