mlsanityData to 14 Oct 2026

Mitigating Distribution Sharpening in Math RLVR via Distribution-Aligned Hint Synthesis and Backward Hint Annealing

arXiv 2604.07747 ↗First posted 9 Apr 2026cs.AI cs.CL cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

In reinforcement learning with verifiable rewards (RLVR), challenging math questions whose sampled groups remain uniformly incorrect yield no informative update, letting easier questions dominate actor updates. This imbalance can contribute to distribution sharpening: low-k accuracy improves while problem coverage contracts. Hint scaffolding can restore informative updates by revealing a prefix of a verified teacher solution. However, existing hint-based approaches still face two issues: teacher--student distribution mismatch and the need to reduce hint exposure to match no-hint evaluation. We address these issues through two components. Distribution-Aligned Hint Synthesis (DAHS) constructs verified teacher hints conditioned on student-style responses. Backward Hint Annealing (BHA) anneals hint exposure across length buckets as bucket accuracy improves. BHA also uses per-question hint dropout to keep no-hint rollouts throughout RL training. Under the DAPO training framework, our method improves both pass@1 and pass@2048 over DAPO on AIME24/25/26 for Qwen3-1.7B-Base and Qwen3-4B-Base, and improves pass@1 on five secondary math benchmarks (Olympiad, MATH500, Minerva Math, AMC23, and GSM8K). On Llama-3.2-1B-Instruct, the gains concentrate in the high-k regime without recovering the base model's coverage. Overall, when verified teacher prefixes are available, DAHS+BHA mitigates DAPO-induced contraction of high-k problem coverage in the evaluated challenging-math RLVR settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 9 Apr 2026.

Builds on

21 of 27 references.

References · 21

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  5. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  6. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 2,057
  7. 2025Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?cited by 982
  8. 2025SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wildcited by 511
  9. 2025Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMscited by 289
  10. 2025Learning to Reason under Off-Policy Guidancecited by 281
  11. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 212
  12. 2025On-Policy RL Meets Off-Policy Experts: Harmonizing Supervised Fine-Tuning and Reinforcement Learning via Dynamic Weightingcited by 91
9 more
  1. 2018Learning Montezuma's Revenge from a Single Demonstrationcited by 80
  2. 2025Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpeningcited by 80
  3. 2024Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learningcited by 65
  4. 2025BREAD: Branched Rollouts from Expert Anchors Bridge SFT & RL for Reasoningcited by 63
  5. 2025Blending Supervised and Reinforcement Fine-Tuning with Prefix Samplingcited by 60
  6. 2025UFT: Unifying Supervised and Reinforcement Fine-Tuningcited by 59
  7. 2025Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoningcited by 52
  8. 2025Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoningcited by 19
  9. 2025Don't Tell the Answer, Truly Guide the Reasoning During RL Rolloutscited by 14

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Unlocking the Unsolvable: Teacher-Guided Curriculum for Data-Efficient RLVR
  2. 2025Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding
  3. 2026Hints, Critics, and Teachers: Prior Injection for Sparse-Reward RL in Vision-Language Math Reasoning
  4. 2026Teach to Learn: Hint Annealing for Self-improving LLM Reasoning
  5. 2026Learning to Hint for Reinforcement Learning
  6. 2025StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason
  7. 2026Curriculum Reinforcement Learning Can Incentivize Reasoning Capacity in LLMs Beyond the Base Model
  8. 2026Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design