mlsanityData to 7 Oct 2026

Beyond Distribution Sharpening: The Importance of Task Rewards

arXiv 2604.16259 ↗First posted 17 Apr 2026cs.LG cs.AIcited by 0

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into their training pipelines, enabling systems to evolve from pure reasoning models into sophisticated agents. However, debate persists regarding whether RL genuinely instills new skills within a base model or merely sharpens its existing distribution to elicit latent capabilities. To address this dichotomy, we present an explicit comparison between distribution sharpening and task-reward-based learning, utilizing RL as a tool to implement both paradigms. Our analysis reveals the inherent limitations of distribution sharpening, demonstrating from first principles how and why the optima can be unfavorable and the approach fundamentally unstable. Furthermore, our experiments using Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct and Qwen3-4B-Instruct-2507 on math datasets confirm that sharpening yields limited gains, whereas incorporating task-based reward signal can greatly help achieve robust performance improvements and stable learning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 36 of its 48 references that are in the map. First posted on arXiv 17 Apr 2026.

Builds on

36 of 48 references.

Closest ancestors · 11

Same direction, newest first

  1. 2026Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening29 Jan
  2. 2025A Comedy of Estimators: On KL Regularization in RL Training of LLMs26 Dec
  3. 2025Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model21 Oct
  4. 2025Reasoning with Sampling: Your Base Model is Smarter Than You Think16 Oct
  5. 2025Spurious Rewards: Rethinking Training Signals in RLVR12 Jun
  6. 2025Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening3 Jun
  7. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  8. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  9. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMs22 Jan
  10. 2023Let's Verify Step by Step31 May
  11. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 25

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  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. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  6. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  7. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,359
  8. 2025OpenAI GPT-5 System Cardcited by 910
  9. 2026Kimi K2.5: Visual Agentic Intelligencecited by 449
  10. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  11. 2025MathArena: Evaluating LLMs on Uncontaminated Math Competitionscited by 275
  12. 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 260
13 more
  1. 2025MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attentioncited by 248
  2. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 244
  3. 2018Correcting Length Bias in Neural Machine Translationcited by 119
  4. 2024Self-Improvement in Language Models: The Sharpening Mechanismcited by 102
  5. 2023Amortizing intractable inference in large language modelscited by 94
  6. 2025The Art of Scaling Reinforcement Learning Compute for LLMscited by 91
  7. 2016Variational inference for Monte Carlo objectivescited by 83
  8. 2025Reinforcement Learning from Human Feedbackcited by 53
  9. 2018Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translationcited by 29
  10. 2025On a few pitfalls in KL divergence gradient estimation for RLcited by 27
  11. 2026Composer 2 Technical Reportcited by 20
  12. 2018A Stable and Effective Learning Strategy for Trainable Greedy Decodingcited by 12
  13. 2026When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewardscited by 4

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  3. 2026Demystifying Reinforcement Learning Post-Training of Language Models
  4. 2025Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
  5. 2026New Skills or Sharper Primitives? A Probabilistic Perspective on the Emergence of Reasoning in RLVR
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