mlsanityData to 14 Oct 2026

Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs

arXiv 2510.04140 ↗First posted 5 Oct 2025cs.AI cs.CLcited by 7

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Models (LLMs). However, the effectiveness of RLVR strongly depends on the capability of base models. This issue arises because it requires the model to have sufficient capability to perform high-quality exploration, which involves both effectiveness and diversity. Unfortunately, existing methods address this issue by imitating expert trajectories, which improve effectiveness but neglect diversity. To address this, we argue that the expert only needs to provide guidance only at critical decision points rather than the entire reasoning path. Based on this insight, we propose MENTOR: Mixed-policy Expert Navigation for Token-level Optimization of Reasoning, a framework that provides expert guidance only at critical decision points to perform effective and diverse exploration in RLVR. Extensive experiments show that MENTOR enables models capture the essence of expert strategies rather than surface imitation, thereby performing high-quality exploration and achieving superior overall performance. Our code is available online.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 33 of its 41 references that are in the map. First posted on arXiv 5 Oct 2025.

Builds on

33 of 41 references.

Closest ancestors · 14

Same direction, newest first

  1. 2025Outcome-based Exploration for LLM Reasoning8 Sep
  2. 2025Pass@k Training for Adaptively Balancing Exploration and Exploitation of Large Reasoning Models14 Aug
  3. 2025QuestA: Expanding Reasoning Capacity in LLMs via Question Augmentation17 Jul
  4. 2025Blending Supervised and Reinforcement Fine-Tuning with Prefix Sampling2 Jul
  5. 2025BREAD: Branched Rollouts from Expert Anchors Bridge SFT & RL for Reasoning20 Jun
  6. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning2 Jun
  7. 2025The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models28 May
  8. 2025From Reasoning to Code: GRPO Optimization for Underrepresented Languages20 May
  9. 2025Learning to Reason under Off-Policy Guidance21 Apr
  10. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?18 Apr
  11. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  12. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMs22 Jan
2 more
  1. 2024Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning10 Oct
  2. 2024AlphaMath Almost Zero: Process Supervision without Process6 May

Other references · 19

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  5. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  7. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,879
  8. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  9. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,848
  10. 2025s1: Simple test-time scalingcited by 1,341
  11. 2023Accelerating Large Language Model Decoding with Speculative Samplingcited by 1,006
  12. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 396
7 more
  1. 2025Deep Think with Confidencecited by 205
  2. 2024Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8Bcited by 158
  3. 2025On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weightingcited by 91
  4. 2025GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learningcited by 34
  5. 2025CER: Confidence Enhanced Reasoning in LLMscited by 17
  6. 2024Think Thrice Before You Act: Progressive Thought Refinement in Large Language Modelscited by 7
  7. 2025Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scalingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewardscited by 6
  2. 2026Experience Augmented Policy Optimization for LLM Reasoningcited by 2
  3. 2026Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVRcited by 2
  4. 2026ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samplescited by 0
  5. 2026MInTRL: Off-policy Intervention can boost On-policy RLcited by 0
  6. 2026Learning Beyond What You Sample: Off-Policy-Aware Cross-Model Trajectory Exchange for RLVRcited by 0
  7. 2026Guide, Then Let Go: Gap-Adaptive Teacher Scheduling for Sparse-Reward Agentic RLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR
  2. 2026SAGE: Shaping Anchors for Guided Exploration in RLVR of LLMs
  3. 2025Diversity-Incentivized Exploration for Versatile Reasoning
  4. 2025Guiding Exploration in Reinforcement Learning Through LLM-Augmented Observations
  5. 2025Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration
  6. 2026Boosting LLM Exploration via Weak-Model Guidance in RLVR
  7. 2026Back to Basics: Revisiting Exploration in Reinforcement Learning for LLM Reasoning via Generative Probabilities
  8. 2026Controllable Exploration in Hybrid-Policy RLVR for Multi-Modal Reasoning