CurateEvo: Data-Curation Evolving for Agentic Post-Training

# CurateEvo: Data-Curation Evolving for Agentic Post-Training
Source: [https://arxiv.org/abs/2607.06140](https://arxiv.org/abs/2607.06140)
[View PDF](https://arxiv.org/pdf/2607.06140)

> Abstract:Large language model \(LLM\) agents require post\-training methods that can improve long\-horizon decision making from environment feedback\. However, existing agentic post\-training pipelines often treat data curation as a fixed preprocessing step, focusing mainly on data augmentation while neglecting filtering, refinement, and adaptation to downstream failures\. We propose CurateEvo, a failure\-driven dynamic evolution framework for agentic post\-training data curation\. CurateEvo represents the curation strategy as executable code and iteratively rewrites it using failed trajectories from a held\-out development set\. At each epoch, the evolved strategy transforms a fixed raw corpus into supervised fine\-tuning data, reinforcement learning data, and an inference\-time memory bank\. The evolution process first improves effectiveness by diagnosing recurring failure modes and augmenting, filtering, or refining data accordingly, and then improves efficiency by pruning redundant or low\-utility training turns under a cost\-aware objective\. Experiments on ACEBench\-Agent, BFCL\-V4, and \{\\tau\}^2\-Bench under both labeled and wild\-data settings show that CurateEvo consistently outperforms prior curation methods, improving average scores by 3\.2 and 2\.7 points, respectively\. Further analyses demonstrate that CurateEvo is compatible with different post\-training recipes and substantially reduces curation overhead\.

## Submission history

From: Dingzirui Wang \[[view email](https://arxiv.org/show-email/88252332/2607.06140)\] **\[v1\]**Tue, 7 Jul 2026 11:07:00 UTC \(366 KB\)