mlsanityData to 14 Oct 2026

Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering

arXiv 2411.11504 ↗First posted 18 Nov 2024cs.AI cs.CL stat.MLcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 101 of its 127 references that are in the map. First posted on arXiv 18 Nov 2024.

Builds on

101 of 127 references.

References · 101

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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38 more
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50 most cited of 101 All 101 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 7.

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  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  2. 2025Reasoning Language Models: A Blueprintcited by 42
  3. 2025DeepRAG: Thinking to Retrieve Step by Step for Large Language Modelscited by 38
  4. 2025A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematicscited by 11
  5. 2026Multi-Rollout On-Policy Distillation via Peer Successes and Failurescited by 10
  6. 2026Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learningcited by 6
  7. 2026Aligning Agentic World Models via Knowledgeable Experience Learningcited by 5
  8. 2026Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysiscited by 5
  9. 2026Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rolloutscited by 5
  10. 2025Communication and Verification in LLM Agents towards Collaboration under Information Asymmetrycited by 3
  11. 2025Beyond Theorem Proving: Formulation, Framework and Benchmark for Formal Problem-Solvingcited by 2
  12. 2025IRPO: Boosting Image Restoration via Post-training GRPOcited by 2
4 more
  1. 2026ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulationcited by 2
  2. 2026Exploring Autonomous Agentic Data Engineering for Model Specializationcited by 2
  3. 2025Calibrated Reasoning: An Explanatory Verifier for Dynamic and Efficient Problem-Solvingcited by 1
  4. 2025Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoningcited by 0