SWE-Verifier：只验证不到 4% 的步骤，让编码智能体更省钱
SWE-VERIFIER: Selective Verification for Cost-Efficient Coding Agents
给编码智能体加了个只有 4B 的小验证器，只挑关键步骤做验证，解决率涨一两个点，成本几乎没变，还能直接用在没见过的模型上。
SWE-Verifier 是一个基于 Qwen3.5-4B 训练的轻量验证模型，通过给编码智能体的动作打分，只在必要时触发候选采样和验证。在 mini-swe-agent 框架下，它把 MiniMax-M2.7 的解决率从 75.47% 提升到 77.20%，成本仅为 1.09 倍。该模型还能泛化到未经训练的 DeepSeek-V4-Flash，解决率从 72.60% 提升到 74.27%，成本 1.13 倍。整个过程中它只验证不到 4% 的步骤，成本远低于全步骤验证的基线方法。模型、代码和数据已在 GitHub 开源。
SWE-VERIFIER: Selective Verification for Cost-Efficient Coding Agents
Coding agents spend substantial computation generating actions and processing long interaction histories. Poor action choices can steer agents into low-value states, wasting computation and increasing API costs. Sampling and verifying alternative actions can improve the agent's decisions, but applying this procedure at every step adds substantial overhead. We introduce SWE-Verifier, a lightweight generative model that selectively triggers candidate sampling and verification. It scores the coding agent's proposed action to determine when additional candidate sampling is needed, then ranks the resulting actions before execution. We train SWE-Verifier from Qwen3.5-4B using preference data that groups contrasting actions with their resulting environment observations and rubric-guided teacher scores. Using the mini-swe-agent harness, SWE-Verifier improves resolution from 75.47% to 77.20% on MiniMax-M2.7 at 1.09 times cost and generalizes to DeepSeek-V4-Flash without training on its actions, improving resolution from 72.60% to 74.27% at 1.13 times cost. Across both models, SWE-Verifier verifies fewer than 4% of steps and achieves comparable or higher resolution than the trajectory and all-step baselines at substantially lower cost. These results demonstrate that selective candidate sampling and lightweight verification provide complementary gains in coding-agent performance and inference efficiency. Our model, code, and data are available at https://github.com/zkjzou/SWE-Verifier.