mlsanityData to 13 Oct 2026

Are complicated loss functions necessary for teaching LLMs to reason?

arXiv 2603.18756 ↗First posted 19 Mar 2026cs.LG cs.AI cs.CLcited by 1

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Recent advances in large language models (LLMs) highlight the importance of post training techniques for improving reasoning and mathematical ability. Group Relative Policy Optimization (GRPO) has shown promise in this domain by combining group relative advantage estimation, PPO style clipping, and KL regularization. However, its complexity raises the question of whether all components are necessary for fostering reasoning behaviors. We conduct a systematic analysis of GRPO and identify two key findings: (1) incorporating negative feedback is essential training solely on actions above a baseline limits learning; and (2) PPO style constraints, such as policy ratio clipping, are not required to improve mathematical reasoning or performance. Building on these insights, we propose REINFORCE with Group Relative Advantage (RGRA), a simplified variant that retains group relative advantage estimation but removes PPO style clipping and policy ratio terms. Experiments across standard mathematical benchmarks indicate that RGRA has the potential to achieve stronger performance than GRPO. Our results suggest that simpler REINFORCE based approaches can effectively enhance reasoning in LLMs, offering a more transparent and efficient alternative to GRPO.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 38 of its 58 references that are in the map. First posted on arXiv 19 Mar 2026.

Builds on

38 of 58 references.

Closest ancestors · 13

Same direction, newest first

  1. 2025GTPO: Stabilizing Group Relative Policy Optimization via Gradient and Entropy Control5 Aug
  2. 2025Prefix Grouper: Efficient GRPO Training through Shared-Prefix Forward5 Jun
  3. 2025Rewarding the Unlikely: Lifting GRPO Beyond Distribution Sharpening3 Jun
  4. 2025The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models28 May
  5. 2025Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMs19 May
  6. 2025S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models12 May
  7. 2025CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models28 Mar
  8. 2025Understanding R1-Zero-Like Training: A Critical Perspective26 Mar
  9. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scale18 Mar
  10. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
  11. 2024Improve Mathematical Reasoning in Language Models by Automated Process Supervision5 Jun
  12. 2023RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment13 Apr
1 more
  1. 2017Proximal Policy Optimization Algorithms20 Jul

Other references · 25

  1. 2023GPT-4 Technical Reportcited by 18,278
  2. 2024The Llama 3 Herd of Modelscited by 14,486
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  4. 2021Training Verifiers to Solve Math Word Problemscited by 9,916
  5. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,091
  6. 2020Measuring Massive Multitask Language Understandingcited by 7,876
  7. 2024Qwen2.5 Technical Reportcited by 4,840
  8. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,691
  9. 2022Emergent Abilities of Large Language Modelscited by 2,499
  10. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,940
  11. 2024Mixtral of Expertscited by 1,811
  12. 2024OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problemscited by 1,111
13 more
  1. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,093
  2. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,077
  3. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 913
  4. 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 848
  5. 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 705
  6. 2019Explain Yourself! Leveraging Language Models for Commonsense Reasoningcited by 367
  7. 2023Statistical Rejection Sampling Improves Preference Optimizationcited by 322
  8. 2023Secrets of RLHF in Large Language Models Part I: PPOcited by 228
  9. 2024Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMscited by 107
  10. 2023CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?cited by 89
  11. 2024MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipelinecited by 81
  12. 2024Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challengescited by 18
  13. 2025Evaluation of LLMs for mathematical problem solvingcited by 7

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2025Mitigating Think-Answer Mismatch in LLM Reasoning Through Noise-Aware Advantage Reweighting
  2. 2025Can GRPO Help LLMs Transcend Their Pretraining Origin?
  3. 2025Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoning
  4. 2025AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin
  5. 2025Comparative Analysis and Parametric Tuning of PPO, GRPO, and DAPO for LLM Reasoning Enhancement
  6. 2026iGRPO: Self-Feedback-Driven LLM Reasoning
  7. 2025A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce
  8. 2026GRPO and Reflection Reward for Mathematical Reasoning in Large Language Models