Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
Process Reward Models (PRMs) have achieved remarkable success in augmenting the reasoning capabilities of Large Language Models (LLMs) within static domains such as mathematics. However, their potential in dynamic data analysis tasks remains underexplored. In this work, we first present a empirical study revealing that general-domain PRMs struggle to supervise data analysis agents. Specifically, they fail to detect silent errors, logical flaws that yield incorrect results without triggering interpreter exceptions, and erroneously penalize exploratory actions, mistaking necessary trial-and-error exploration for grounding failures. To bridge this gap, we introduce DataPRM, a novel environme...
| Authors | Zhisong Qiu, Shuofei Qiao, Kewei Xu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 April 2026 |
| Last updated at source | 20 June 2026 |
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