CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks
Score
75提出求解器相对的难度校准新概念，填补了可执行任务未必可学习的空白。
方法设计完整，包含消融与多基准迁移实验，验证逻辑清晰。
可自动生成高质量 Agent 训练数据，对数据工程和模型训练有直接参考价值。
当前 Agent 训练数据稀缺，该工作回应了前沿且紧迫的需求。
论文发布于 arXiv 且附有可复现实验设置，属于可信学术来源。
Base 83 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
The method is innovative and the experiments are solid, with cross-benchmark transfer gains validating the practical value of solver-relative learnability.
AI commentary
「This paper is worth attention because it shifts task difficulty from human-defined to solver-behavior-defined, which is highly relevant for automated data synthesis. Teams working on agent training data, especially terminal-operation agents, should carefully study its calibration strategy and transfer results.」
Quick overview
CalibForge presents an autonomous terminal-task synthesis system that revises candidate tasks through adversarial solver calibration using verified solver behavior. Multi-solver calibration targets disagreement within a heterogeneous solver pool, while contrastive calibration targets a strong-pass/weak-fail relation, both operationalizing a solver-relative learnable zone. Using CalibForge, the authors construct 5,431 calibrated terminal tasks. Ablations show that both strategies yield more effective supervision than authoring and validation alone or single-solver feedback. Models trained on the full collection achieve 32.58% and 47.57% on Terminal-Bench 2.0, with the largest improvements reaching 24.71 points on Terminal-Bench 2.0, 27.68 points on SWE-bench Pro, and 30.04 points on Doc2Repo. These results support solver-relative learnability as a practical target for constructing effective and transferable agent training data.
- Proposes adversarial solver calibration using multi-solver disagreement and strong-pass/weak-fail contrast to define task difficulty.
- Constructs 5,431 calibrated terminal tasks, significantly outperforming authoring/validation alone and single-solver feedback.
- Achieves up to 24.71, 27.68, and 30.04 percentage-point improvements on Terminal-Bench 2.0, SWE-bench Pro, and Doc2Repo respectively.
Full text
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