GRPO-MA: Multi-Answer Generation in GRPO for Stable and Efficient
  Chain-of-Thought Training
Authors: name: Hongcheng Wang, name: Yinuo Huang, name: Sukai Wang, name: Guanghui Ren, name: Hao Dong

Recent progress, such as DeepSeek-R1, has shown that the GRPO algorithm, a
Reinforcement Learning (RL) approach, can effectively train Chain-of-Thought
(CoT) reasoning in Large Language Models (LLMs) and Vision-Language Models
(VLMs). In this paper, we analyze three challenges of GRPO: gradient coupling
between thoughts and answers, sparse reward signals caused by limited parallel
sampling, and unstable advantage estimation. To mitigate these challenges, we
propose GRPO-MA, a simple yet theoretically grounded method that leverages
multi-answer generation from each thought process, enabling more robust and
efficient optimization. Theoretically, we show that the variance of thought
advantage decreases as the number of answers per thought increases.
Empirically, our gradient analysis confirms this effect, showing that GRPO-MA
reduces gradient spikes compared to GRPO. Experiments on math, code, and
diverse multimodal tasks demonstrate that GRPO-MA substantially improves
performance and training efficiency. Our ablation studies further reveal that
increasing the number of answers per thought consistently enhances model
performance.