mlsanityData to 14 Oct 2026

Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding

arXiv 2509.06923 ↗First posted 8 Sep 2025cs.LGcited by 16

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, existing RLVR methods often suffer from exploration inefficiency due to mismatches between the training data's difficulty and the model's capability. LLMs fail to discover viable reasoning paths when problems are overly difficult, while learning little new capability when problems are too simple. In this work, we formalize the impact of problem difficulty by quantifying the relationship between loss descent speed and rollout accuracy. Building on this analysis, we propose SEELE, a novel supervision-aided RLVR framework that dynamically adjusts problem difficulty to stay within the high-efficiency region. SEELE augments each training sample by appending a hint (part of a full solution) after the original problem. Unlike previous hint-based approaches, SEELE deliberately and adaptively adjusts the hint length for each problem to achieve an optimal difficulty. To determine the optimal hint length, SEELE employs a multi-round rollout sampling strategy. In each round, it fits an item response theory model to the accuracy-hint pairs collected in preceding rounds to predict the required hint length for the next round. This instance-level, real-time difficulty adjustment aligns problem difficulty with the evolving model capability, thereby improving exploration efficiency. Experimental results show that SEELE outperforms Group Relative Policy Optimization (GRPO) and Supervised Fine-tuning (SFT) by +11.8 and +10.5 points, respectively, and surpasses the best previous supervision-aided approach by +3.6 points on average across six math reasoning benchmarks.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 3 of its 43 references that are in the map. First posted on arXiv 8 Sep 2025.

Builds on

3 of 43 references.

References · 3

  1. 2025A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?cited by 223
  2. 2025SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Modelscited by 212
  3. 2025On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weightingcited by 91

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 13.

16 papers.

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  1. 2025ADHint: Adaptive Hints with Difficulty Priors for Reinforcement Learningcited by 12
  2. 2025Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learningcited by 11
  3. 2026Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Informationcited by 7
  4. 2026EchoRL: Reinforcement Learning via Rollout Echoingcited by 4
  5. 2026Zone of Proximal Policy Optimization: Teacher in Prompts, Not Gradientscited by 3
  6. 2026Where Rollouts Begin: Low-Load, High-Leverage First-Token Diversification for RLVRcited by 2
  7. 2025Variance-Aware Baselines and Adaptive Learning Rates for Reinforcement Learning with Verifiable Rewardscited by 1
  8. 2026UltraLogic: Enhancing LLM Reasoning through Large-Scale Data Synthesis and Bipolar Float Rewardcited by 1
  9. 2026Thinking Seeds: Leveraging Historical Diversity for Position-Aware RL in LLMscited by 1
  10. 2026Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regimecited by 1
  11. 2026Agent-G2: Gaussian Guidance for Agentic Reinforcement Learningcited by 1
  12. 2026Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scalingcited by 0
4 more
  1. 2026AdaKP: Online Adaptive Knowledge-Point Selection for Reasoning-Oriented Reinforcement Learningcited by 0
  2. 2026Unlocking the Unsolvable: Teacher-Guided Curriculum for Data-Efficient RLVRcited by 0
  3. 2026STRETCH the Boundaries: A Unified Self-Taught Framework for Progressive LLM Evolutioncited by 0
  4. 2026Balancing Reference Guidance and Free Generation in Trajectory Rollouts for Reasoning RLcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement
  2. 2025StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason
  3. 2025Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts
  4. 2025Adaptive Guidance Accelerates Reinforcement Learning of Reasoning Models
  5. 2025Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
  6. 2025From Trial-and-Error to Improvement: A Systematic Analysis of LLM Exploration Mechanisms in RLVR
  7. 2025RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
  8. 2025RAVR: Reference-Answer-guided Variational Reasoning for Large Language Models