Nvidia 论文：用「首个通过测试的编辑」预测基座模型的 agent 潜力，相关性 0.964
rohanpaul_ai · x · 2026-10-12
- Nvidia paper proposes replaying successful agent trajectories to find the first edit that makes tests pass, then scoring base models on that decisive step.
- Three screens—BPB, Patch MCCQ, prefix-conditioned pass@K—ranked 10 model pairs in agreement with post-trained SWE-bench Verified pass@1 at 0.964.
- Practical takeaway: before paying for expensive post-training, run this check with only a benchmark's successful runs and its verifier.
所属事件:Nvidia 提出回放法预测基座模型的编码 agent 潜力(2 条相关)→
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