Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0
Score
78揭示了一次性评估的陷阱并提出了复合增益的实证证据，对Agent社区有高信息增量。
实验设计对比充分，结果清晰，论证逻辑严谨，质量上乘。
对实际部署Agent的持续优化有直接指导意义，但方法复杂度可能较高。
论文为最新预印本，持续学习与Agent优化是当前热点，时效性强。
arXiv预印本，实验细节公开，可复现，且来自可靠研究机构（推测为学术界）。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper features rigorous experimental design, identifies the core reason for current methods' failure in practice, and provides a reusable technical path for continual agent optimization.
AI commentary
「This paper hits the biggest blind spot in current agent optimization evaluation: one-shot tests fail to capture real-world continuous changes. If you are building or deploying long-running agent systems, this paper is worth careful reading—the regression control idea in RELAI-VCL may become a standard component in future designs. For AI researchers, the experimental comparison and problem-finding methodology are also very enlightening.」
Quick overview
This paper challenges the one-shot evaluation of agent optimization methods on static benchmarks and proposes a two-phase continual-learning framework. On hard tasks from Terminal-Bench 2.0, the authors compare three methods: GEPA, Meta Harness, and RELAI-VCL. Results show that only RELAI-VCL, which incorporates regression control, achieves compounding gains—positive transfer to unseen tasks and continued improvement after new tasks are added, reaching a lifelong average pass rate of 76.4%, significantly outperforming baselines.
- Existing agent optimization methods are typically evaluated in a one-shot static setting, ignoring real-world continuous task arrival.
- The paper introduces a two-phase continual-learning evaluation to test whether optimization gains compound on new tasks.
- Only RELAI-VCL, which includes regression control, achieves both positive transfer and continued improvement.
- RELAI-VCL achieves 76.4% lifelong average pass rate, while GEPA and Meta Harness fall below baseline or fail to further optimize.
Full text
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