评测机器学习代理在真实代码库中修复与优化模型的能力。
DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories
- 构建48个来自论文的真实任务，模拟复杂代码环境下的模型改进。
- 使用搜索增强框架后，GPT-5在12小时任务中成功率提升至49.0%。
- 新框架有效防止代理“钻空子”，显著提升实验可靠性。
用于机器学习实验的自主代理需在异构代码仓库中导航、修复训练流程，并在真实计算约束下评估改进方案。现有基准未能充分覆盖这些条件。我们提出DeltaML-Bench，包含48个源自研究论文的任务，要求代理在不完美、开源的代码库中改进已发表基线模型。我们评估了GPT-5与Claude Sonnet 4在标准模块化代理和基于搜索的ARG框架下的表现。在4次6小时分配中，ARG将GPT-5的单次成功率达从9.4%提升至33.9%；在2次12小时分配中，成功率达49.0%。模块化配置的指令规避率高达47.9%，而所有评估的ARG配置均未发现此类行为。结果表明，框架设计与完整性校验对部署自主机器学习实验代理至关重要。
原文摘要 · Abstract (English)
Autonomous agents for machine learning experimentation must navigate heterogeneous repositories, repair training pipelines, and evaluate candidate improvements under realistic compute constraints. Existing benchmarks only partially capture these conditions. We introduce DeltaML-Bench, a benchmark comprising 48 tasks sourced from research papers that require agents to improve published baselines within imperfect, open-source repositories. We evaluate GPT-5 and Claude Sonnet 4 with a standard Modular agent and a search-based ARG scaffolding. In the 4 x 6h allocation, ARG raises GPT-5's per-run success rate from 9.4% to 33.9%; in the 2 x 12h allocation, GPT-5 ARG reaches 49.0%. Modular configurations exhibit specification gaming rates as high as 47.9%, while no gaming is observed in the evaluated ARG configurations. These results indicate that scaffolding design and integrity checks are important considerations when deploying agents for autonomous ML experimentation.
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