mlsanityData to 7 Oct 2026

Know Thy Reasoner: Not All Language Models Explore Alike

arXiv 2604.10827 ↗First posted 12 Apr 2026cs.AIcited by 0

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

Abstract (arXiv)

Compute scaling for LLM reasoning trades off exploring solution approaches (breadth) against refining promising ones (depth), yet why a given trade-off works, and why it often fails to transfer across models, remains unclear. We argue that textbfthe optimal strategy depends on the model's diversity profile, the spread of probability mass across solution approaches, and that this must be characterized before any exploration strategy is adopted. We formalize this with a framework decomposing reasoning uncertainty, deriving when depth-based refinement outperforms parallel sampling, and validate it across three model families at both inference and training. Our central finding is that the diversity regime dictates the strategy: low-diversity aligned models benefit from depth-based refinement with lightweight intrinsic signals, whereas high-diversity base models are often harmed by it, and instead need breadth or stronger signals to compensate.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 35 of its 45 references that are in the map. First posted on arXiv 12 Apr 2026.

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35 of 45 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026PRISM: A Unified Framework for Post-Training LLMs Without Verifiable Rewards8 Jan
  2. 2025Chain-in-Tree: Back to Sequential Reasoning in LLM Tree Search30 Sep
  3. 2025Learning a Continue-Thinking Token for Enhanced Test-Time Scaling12 Jun
  4. 2025A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search10 Jun
  5. 2025s1: Simple test-time scaling31 Jan
  6. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug
  7. 2023Self-Refine: Iterative Refinement with Self-Feedback30 Mar

Other references · 28

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  4. 2025Qwen3 Technical Reportcited by 8,195
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,678
  7. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 948
  8. 2022Solving math word problems with process- and outcome-based feedbackcited by 871
  9. 2025Group Sequence Policy Optimizationcited by 788
  10. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 386
  11. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 338
  12. 2025DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoningcited by 261
16 more
  1. 2023Large Language Models as Commonsense Knowledge for Large-Scale Task Planningcited by 231
  2. 2025Learning to Reason without External Rewardscited by 199
  3. 2024From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Modelscited by 136
  4. 2025Olmo 3cited by 95
  5. 2025Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learningcited by 84
  6. 2025RLPR: Extrapolating RLVR to General Domains without Verifierscited by 83
  7. 2025TreeRL: LLM Reinforcement Learning with On-Policy Tree Searchcited by 75
  8. 2025Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoningcited by 43
  9. 2025Diversity-Incentivized Exploration for Versatile Reasoningcited by 28
  10. 2025Diverse Preference Learning for Capabilities and Alignmentcited by 25
  11. 2025Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RLcited by 23
  12. 2025Curiosity-Driven Reinforcement Learning from Human Feedbackcited by 14
  13. 2025Count Counts: Motivating Exploration in LLM Reasoning with Count-based Intrinsic Rewardscited by 9
  14. 2025PACR: Progressively Ascending Confidence Reward for LLM Reasoningcited by 9
  15. 2025Exploring Multi-Temperature Strategies for Token- and Rollout-Level Control in RLVRcited by 8
  16. 2025Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answeringcited by 6

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  1. 2024Inference Scaling vs Reasoning: An Empirical Analysis of Compute-Optimal LLM Problem-Solving
  2. 2025Revisiting Test-Time Scaling: A Survey and a Diversity-Aware Method for Efficient Reasoning
  3. 2025Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning
  4. 2025On the Effect of Sampling Diversity in Scaling LLM Inference
  5. 2026Knowing When Thinking Is Not Enough: Teaching Small Reasoning Models to Reason Beyond Their Parametric Knowledge
  6. 2025Outcome-based Exploration for LLM Reasoning
  7. 2023Diversity of Thought Improves Reasoning Abilities of LLMs
  8. 2026Divergent-Convergent Reasoning: Scaling Test-Time Compute through Structured Solution Synthesis