Comparative Analysis and Parametric Tuning of PPO, GRPO, and DAPO for LLM Reasoning Enhancement

• URL: http://arxiv.org/abs/2512.07611v1
• Date: Mon, 08 Dec 2025 14:58:19 GMT
• Title: Comparative Analysis and Parametric Tuning of PPO, GRPO, and DAPO for LLM Reasoning Enhancement
• Authors: Yongsheng Lian,
• Abstract summary: This study presents a systematic comparison of three Reinforcement Learning (RL) algorithms for improving complex reasoning in large language models (LLMs)<n>We find that RL-trained models outperform their corresponding base models, although the degree of improvement differs by benchmark.<n>Increasing the group size in GRPO and DAPO leads to more stable training dynamics and higher accuracy, while the impact of the KL-penalty coefficient is non-monotonic.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a systematic comparison of three Reinforcement Learning (RL) algorithms (PPO, GRPO, and DAPO) for improving complex reasoning in large language models (LLMs). Our main contribution is a controlled transfer-learning evaluation: models are first fine-tuned on the specialized Countdown Game and then assessed on a suite of general-purpose reasoning benchmarks. Across all tasks, RL-trained models outperform their corresponding base models, although the degree of improvement differs by benchmark. Our parametric analysis offers practical guidance for RL-based LLM training. Increasing the group size in GRPO and DAPO leads to more stable training dynamics and higher accuracy, while the impact of the KL-penalty coefficient is non-monotonic. Additionally, we find that the Dynamic Sampling (DS) component in DAPO does not improve performance; in fact, the best overall results are achieved with DAPO when DS is disabled.

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