Claude Opus 5 在 RSIGym 中将 Qwen 模型 SWE-bench Verified 成绩提升近三倍
有人做了个开源环境让 AI 智能体自己训练模型，Claude Opus 5 把 Qwen 的 SWE-bench 成绩刷到近三倍，还发现修 harness 比堆训练更管用。
Evolvent_AI 发布开源环境 RSIGym，让 AI 智能体在统一预算内完成模型微调、部署和基准测试。主测试中 6 个前沿智能体从 Qwen3.5-35B-A3B-Base 出发，每个基准配置 500 美元服务预算，Claude Opus 5 将 SWE-bench Verified 成绩提升近三倍。所有智能体从纯 CPU 容器运行，通过远程服务调用 LoRA 微调、模型服务与沙箱。10 次小规模测试中有 8 次显示加重训练反而得分更低，先修复 harness 更有效。
Claude Opus 5 nearly tripled a Qwen model's SWE-bench Verified score while working as an automated AI researcher.
@Evolvent_AI 's newly released open-source RSIGym made that measurement possible by letting an AI agent retrain a model and rewrite its harness inside one budgeted environment.
shows that frontier AI agents can substantially improve another AI model when training, serving, and testing come as ready-made services.
Agents work from CPU-only containers and call remote services for LoRA fine-tuning, model serving, benchmarking and sandboxes, all charged against a per-run budget.
In the main test, 6 frontier agents started from Qwen3.5-35B-A3B-Base and a minimal harness, with $500 of services per benchmark.
If you're improving an agent, read its failure logs and fix the harness first: heavier training scored lower in 8 of 10 small tests.
