Controllable and Verifiable Process Data Synthesis for Process Reward Models 事件

Controllable and Verifiable Process Data Synthesis for Process Reward Models arXiv:2605.02395v2 Announce Type: replace Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error location, error type, and trajectory consistency. We propose a controllable and verifiable framework for synthesizing process supervision data for PRMs. Our framework first constructs a correct symbolic reasoning chain,