mlsanityData to 7 Oct 2026

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv 2606.04503 ↗First posted 3 Jun 2026cs.LG cs.AIcited by 1

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset. To this end, data-efficient RLVR methods have been widely studied from two perspectives: (i) data selection methods identify a small subset of "golden" samples that yield near-full-data performance, but they rely on a pre-existing pool of labeled data. (ii) unsupervised RLVR methods train the model using its own internal supervision signals on large-scale unlabeled data, yet they exhibit suboptimal performance. Accordingly, we investigate the "pick in the dark" setup for RLVR, which aims to select, without prior supervision, unlabeled samples that are most beneficial for training and worthy of annotation. Through systematic analysis, we demonstrate that smart picks hinge on a well-calibrated uncertainty estimator to enable strategic partitioning of data for adaptive training regimes. Building on this insight, we propose PivotTrace, a three-way data triage framework that leverages attention dynamics to trace metacognitive pivots during reasoning. By precisely quantifying uncertainty through pivot density, PivotTrace achieves automated data routing to synergistically maximize both annotation and training efficiency. Empirically, PivotTrace surpasses the fully supervised LRM with only 29.3% annotated samples and 2.75 faster convergence.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 50 of its 72 references that are in the map. First posted on arXiv 3 Jun 2026.

Builds on

50 of 72 references.

Closest ancestors · 28

Same direction, newest first

  1. 2025TraPO: A Semi-Supervised Reinforcement Learning Framework for Boosting LLM Reasoning15 Dec
  2. 2025Data-Efficient RLVR via Off-Policy Influence Guidance30 Oct
  3. 2025CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs1 Oct
  4. 2025Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models30 Sep
  5. 2025A Survey of Reinforcement Learning for Large Reasoning Models10 Sep
  6. 2025Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward1 Sep
  7. 2025RLPR: Extrapolating RLVR to General Domains without Verifiers23 Jun
  8. 2025No Free Lunch: Rethinking Internal Feedback for LLM Reasoning20 Jun
  9. 2025Reasoning with Exploration: An Entropy Perspective17 Jun
  10. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs17 Jun
  11. 2025Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning10 Jun
  12. 2025Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models5 Jun
16 more
  1. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning2 Jun
  2. 2025The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models28 May
  3. 2025First SFT, Second RL, Third UPT: Continual Improving Multi-Modal LLM Reasoning via Unsupervised Post-Training28 May
  4. 2025Learning to Reason without External Rewards26 May
  5. 2025The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning21 May
  6. 2025Absolute Zero: Reinforced Self-play Reasoning with Zero Data6 May
  7. 2025TTRL: Test-Time Reinforcement Learning22 Apr
  8. 2025Learning to Reason under Off-Policy Guidance21 Apr
  9. 2025Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization8 Apr
  10. 2025Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning4 Apr
  11. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  12. 2025LIMR: Less is More for RL Scaling17 Feb
  13. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  14. 2025REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization4 Jan
  15. 2024Tulu 3: Pushing Frontiers in Open Language Model Post-Training22 Nov
  16. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 22

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,710
  6. 2024OpenAI o1 System Cardcited by 1,220
  7. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,057
  8. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 381
  9. 2025Demystifying Long Chain-of-Thought Reasoning in LLMscited by 323
  10. 2025Scalable Best-of-N Selection for Large Language Models via Self-Certaintycited by 182
  11. 2025SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvementcited by 144
  12. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
10 more
  1. 2025Thought Anchors: Which LLM Reasoning Steps Matter?cited by 126
  2. 2024Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluationcited by 56
  3. 2025CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Processcited by 50
  4. 2023Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Modelscited by 40
  5. 2024Self-Reflection in LLM Agents: Effects on Problem-Solving Performancecited by 38
  6. 2017Active Learning for Convolutional Neural Networks: A Core-Set Approachcited by 34
  7. 2025Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm Enables Fine-Grained Policy Optimizationcited by 23
  8. 2025Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoningcited by 17
  9. 2023FreeAL: Towards Human-Free Active Learning in the Era of Large Language Modelscited by 14
  10. 2025Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoningcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026DE-Venus: A Data-Efficient RLVR Framework for Large Language Modelscited by 0

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  1. 2026Detecting RLVR Training Data via Structural Convergence of Reasoning
  2. 2026Single-Rollout Hidden-State Dynamics for Training-Free RLVR Data Selection
  3. 2026IRDS: Interpretable RLVR Data Selection via Verifier-Coupled Sparse Autoencoder Coverage
  4. 2026On the Implicit Reward Overfitting and the Low-rank Dynamics in RLVR
  5. 2025Generalization of RLVR Using Causal Reasoning as a Testbed
  6. 2025Extending RLVR to Open-Ended Tasks via Verifiable Multiple-Choice Reformulation
  7. 2026SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
  8. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?