ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into aligned assistants. However, recent evaluations reveal that even frontier agents struggle to perform this task. While the success of post-training fundamentally relies on acquiring high-quality data, relying on agents to autonomously curate targeted training datasets from the open web introduces severe challenges. Executing the long-horizon tasks of searching, filtering, and balancing data within noisy web environments frequently overwhelms an agent's limited context, ultimately leading to degraded dataset quality and suboptimal downstrea...
| Authors | Zhengyang Zhao, Shengjie Ye, Lu Ma et al. |
|---|---|
| Primary category | cs.AI |
| Published | 31 May 2026 |
| Last updated at source | 31 May 2026 |
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