Search, Verify and Feedback: Towards Next Generation Post-training
  Paradigm of Foundation Models via Verifier Engineering
Authors: name: Xinyan Guan, name: Yanjiang Liu, name: Xinyu Lu, name: Boxi Cao, name: Ben He, name: Xianpei Han, name: Le Sun, name: Jie Lou, name: Bowen Yu, name: Yaojie Lu, name: Hongyu Lin

The evolution of machine learning has increasingly prioritized the
development of powerful models and more scalable supervision signals. However,
the emergence of foundation models presents significant challenges in providing
effective supervision signals necessary for further enhancing their
capabilities. Consequently, there is an urgent need to explore novel
supervision signals and technical approaches. In this paper, we propose
verifier engineering, a novel post-training paradigm specifically designed for
the era of foundation models. The core of verifier engineering involves
leveraging a suite of automated verifiers to perform verification tasks and
deliver meaningful feedback to foundation models. We systematically categorize
the verifier engineering process into three essential stages: search, verify,
and feedback, and provide a comprehensive review of state-of-the-art research
developments within each stage. We believe that verifier engineering
constitutes a fundamental pathway toward achieving Artificial General
Intelligence.