NVIDIA 论文提出在首个让测试通过的编辑处评估 base 模型，预测编码智能体后训练表现
NVIDIA 新论文提出一种评估 base 模型的方法：重放强智能体的成功运行轨迹，在每次编辑后重跑测试，找到第一个让测试通过的编辑，再检验 base 模型是否倾向该编辑、能否写出可通过的编辑。
New Nvidia paper shows scoring base models on every step of an agent run works worse than scoring them only on the edit that first makes the tests pass.
Post-training is expensive, so picking the right base model matters. Yet agent benchmarks score base models near zero, and simple coding tests barely predict later agent skill.
They replay a strong agent's successful run and rerun the tests after each edit to find the 1st edit that makes them pass. At this decisive step they test whether a base model favors that edit, picks it over failing edits, and can write a passing one.
How likely each base model was to write that exact edit ranked the same 10 models in line with their post-trained SWE-bench Verified scores, at 0.964.
When choosing a base model to post-train for agent work, run this check first. It needs only a benchmark's successful agent runs and its tests.
来源：Rohan Paul · x.com