mlsanityData to 7 Oct 2026

Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning

arXiv 2602.03978 ↗First posted 3 Feb 2026cs.AI cs.LGcited by 4

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

As Large Reasoning Models (LRMs) are increasingly deployed, auditing their chain-of-thought (CoT) traces for safety becomes critical. Recent work has reported that monitorability--the degree to which CoT faithfully and informatively reflects internal computation--can appear as a "free gift" during the early stages of Reinforcement Learning with Verifiable Rewards (RLVR). We make this observation concrete through a systematic evaluation across model families and training domains. Our results show that this effect is not universal: monitorability improvements are strongly data-dependent. In particular, we demonstrate the critical role of data diversity and instruction-following data during RLVR training. We further show that monitorability is orthogonal to capability--improvements in reasoning performance do not imply increased transparency. Through mechanistic analysis, we attribute monitorability gains primarily to response distribution sharpening (entropy reduction) and increased attention to the prompt, rather than stronger causal reliance on reasoning traces. We also reveal how monitorability dynamics vary with controlled training and evaluation difficulty. Together, these findings provide a holistic view of how monitorability emerges under RLVR, clarifying when gains are likely to occur and when they are not.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 30 of its 42 references that are in the map. First posted on arXiv 3 Feb 2026.

Builds on

30 of 42 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models15 Dec
  2. 2025Reasoning with Sampling: Your Base Model is Smarter Than You Think16 Oct
  3. 2025Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective17 Jun
  4. 2025Magistral12 Jun
  5. 2025AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning22 May
  6. 2025Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining10 Apr
  7. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan

Other references · 23

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  4. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,115
  5. 2023Instruction-Following Evaluation for Large Language Modelscited by 1,143
  6. 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
  7. 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
  8. 2025Reasoning Models Don't Always Say What They Thinkcited by 388
  9. 2025Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscationcited by 338
  10. 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetycited by 222
  11. 2025ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoningcited by 186
  12. 2025Generalizing Verifiable Instruction Followingcited by 178
11 more
  1. 2025Skywork Open Reasoner 1 Technical Reportcited by 161
  2. 2025GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Taskscited by 142
  3. 2025Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoningcited by 101
  4. 2025The Invisible Leash: Why RLVR May or May Not Escape Its Origincited by 92
  5. 2025When Chain of Thought is Necessary, Language Models Struggle to Evade Monitorscited by 81
  6. 2025Are DeepSeek R1 And Other Reasoning Models More Faithful?cited by 69
  7. 2025CoT Red-Handed: Stress Testing Chain-of-Thought Monitoringcited by 49
  8. 2025Monitoring Monitorabilitycited by 47
  9. 2025Measuring the Faithfulness of Thinking Drafts in Large Reasoning Modelscited by 22
  10. 2024CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMscited by 18
  11. 2025Reasoning Under Pressure: How do Training Incentives Influence Chain-of-Thought Monitorability?cited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoningcited by 2
  2. 2026Architecture Determines Observability of Transformerscited by 0
  3. 2026Does Out-of-Sight Equal Out-of-Mind in CoT Monitorability?cited by 0
  4. 2026When Confidence Rises Too Early: Detecting Shortcut Reasoning via Premature Answer Commitmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
  2. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?
  3. 2026Detecting RLVR Training Data via Structural Convergence of Reasoning
  4. 2025The Debate on RLVR Reasoning Capability Boundary: Shrinkage, Expansion, or Both? A Two-Stage Dynamic View
  5. 2025Decomposing the Entropy-Performance Exchange: The Missing Keys to Unlocking Effective Reinforcement Learning
  6. 2025The Reasoning Boundary Paradox: How Reinforcement Learning Constrains Language Models
  7. 2026Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
  8. 2026On Language Drift during RLVR Post-Training