mlsanityData to 14 Oct 2026

Dyve: Thinking Fast and Slow for Dynamic Process Verification

arXiv 2502.11157 ↗First posted 16 Feb 2025cs.AIcited by 9

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate token-level confirmation System 1 for straightforward steps and comprehensive analysis System 2 for complex ones. Leveraging a novel step-wise consensus-filtered process supervision technique, combining Monte Carlo estimation with LLM based evaluation, Dyve curates high-quality supervision signals from noisy data. Experimental results on ProcessBench and the MATH dataset confirm that Dyve significantly outperforms existing process-based verifiers and boosts performance in Best-of-N settings.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 15 of its 25 references that are in the map. First posted on arXiv 16 Feb 2025.

Builds on

15 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs30 Dec

Other references · 14

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  3. 2023Let's Verify Step by Stepcited by 3,789
  4. 2024A Survey on LLM-as-a-Judgecited by 1,423
  5. 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,136
  6. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,090
  7. 2023Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotationscited by 928
  8. 2024Generative Verifiers: Reward Modeling as Next-Token Predictioncited by 480
  9. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervisioncited by 441
  10. 2025The Lessons of Developing Process Reward Models in Mathematical Reasoningcited by 415
  11. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 344
  12. 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 313
2 more
  1. 2024ProcessBench: Identifying Process Errors in Mathematical Reasoningcited by 292
  2. 2025Early External Safety Testing of OpenAI's o3-mini: Insights from the Pre-Deployment Evaluationcited by 13

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 8; 2026: 1.

9 papers.

  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  2. 2025GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoningcited by 97
  3. 2025Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropycited by 38
  4. 2025Heimdall: test-time scaling on the generative verificationcited by 26
  5. 2025Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement Learningcited by 16
  6. 2025Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMscited by 13
  7. 2025Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifiercited by 8
  8. 2025Trust but Verify! A Survey on Verification Design for Test-time Scalingcited by 8
  9. 2026Agri-R1: Agricultural Reasoning for Disease Diagnosis via Automated-Synthesis and Reinforcement Learningcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking
  2. 2026interwhen: A Generalizable Framework for Steering Reasoning Models with Test-time Verification
  3. 2024Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
  4. 2026Verify to Amplify: Improving Reasoning via Learned Chain-of-Thought Verification
  5. 2025Temporal Consistency for LLM Reasoning Process Error Identification
  6. 2024LLM2: Let Large Language Models Harness System 2 Reasoning
  7. 2026Process Supervision for Chain-of-Thought Reasoning via Monte Carlo Net Information Gain
  8. 2023DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks