Exploring Next
Exploring Next — Ep 338 w/ Justy & Cody — Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
DataPRM is a process reward model built specifically for agentic data analysis that fixes two critical gaps in general-purpose PRMs: silent errors (code runs but produces wrong results) and grounding errors (penalizing necessary exploration). It works by actively probing the environment to validate intermediate states and using a ternary reward strategy to distinguish between correctable mistakes and irrecoverable failures. The team built a 7K-instance training dataset and show 7-11% improvements on benchmarks with only 4B parameters.