title: Search, Verify and Feedback: Towards Next Generation Post-training
    Paradigm of Foundation Models via Verifier Engineering
summary: 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.
paper_authors._id: 673c296853734795380a3dd4
paper_authors.name: Xinyan Guan
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/9477b9267d5692a4fe59e30590e9639d.svg
paper_authors.fullname: Xinyan Guan
paper_authors.type: user
paper_authors.user: xinyan233333
paper_authors._id: 673c296853734795380a3dd5
paper_authors.name: Yanjiang Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: zuijiang
paper_authors.type: user
paper_authors.user: zuijiang
paper_authors._id: 673c296853734795380a3dd6
paper_authors.name: Xinyu Lu
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/f2e9ee3f1f70bcfabb2b660b241b7aee.svg
paper_authors.fullname: Xinyu Lu
paper_authors.type: user
paper_authors.user: luxinyu
paper_authors._id: 673c296853734795380a3dd7
paper_authors.name: Boxi Cao
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paper_authors.fullname: Boxi Cao
paper_authors.type: user
paper_authors.user: Bowieee
paper_authors._id: 673c296853734795380a3dd8
paper_authors.name: Ben He
paper_authors._id: 673c296853734795380a3dd9
paper_authors.name: Xianpei Han
paper_authors.status: admin_assigned
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paper_authors._id: 65e99a77e71555ed193609cf
paper_authors.avatarUrl: /avatars/38ceb127883944677665da967d17dd18.svg
paper_authors.fullname: Xianpei Han
paper_authors.type: user
paper_authors.user: xphan
paper_authors._id: 673c296853734795380a3dda
paper_authors.name: Le Sun
paper_authors._id: 673c296853734795380a3ddb
paper_authors.name: Jie Lou
paper_authors._id: 673c296853734795380a3ddc
paper_authors.name: Bowen Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T13:08:19.304Z
paper_authors._id: 6438b43ab2ea24b52ebac2b9
paper_authors.avatarUrl: /avatars/84133cd719a4b1e2f5c1a74178425f86.svg
paper_authors.fullname: Bowen Yu
paper_authors.type: user
paper_authors.user: bwy
paper_authors._id: 673c296853734795380a3ddd
paper_authors.name: Yaojie Lu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T13:08:11.873Z
paper_authors._id: 6216496a9b34d2fb49144599
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6216496a9b34d2fb49144599/41CKA_h1Ffj3RzVabSAkm.jpeg
paper_authors.fullname: Yaojie Lu
paper_authors.type: user
paper_authors.user: luyaojie
paper_authors._id: 673c296853734795380a3dde
paper_authors.name: Hongyu Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-19T13:08:05.720Z
paper_authors._id: 6711c702f858a456b4b9f3a4
paper_authors.avatarUrl: /avatars/178e9567c3111ab22717c3c0dd003a6a.svg
paper_authors.fullname: Hongyu  Lin
paper_authors.type: user
paper_authors.user: sanmusunrise
paper_title: Search, Verify and Feedback: Towards Next Generation Post-training
    Paradigm of Foundation Models via Verifier Engineering
paper_summary: 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.
paper_ai_summary: Verifier engineering uses automated verification feedback to enhance foundation models, proposing a critical step towards Artificial General Intelligence.
paper_ai_keywords: verifier engineering post-training paradigm automated verifiers feedback Artificial General Intelligence