[ICLR2026] GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning

Xiangxiang Chu, Hailang Huang, Xiao Zhang, Fei Wei, Yong Wang

AMAP, Alibaba Group

🎉 Congratulations

GPG has been accepted to ICLR 2026 and is supported by the famous VERL RL framework: https://verl.readthedocs.io/en/latest/algo/gpg.html

Reinforcement Learning (RL) can directly enhance the reasoning capabilities of large language models without extensive reliance on Supervised Fine-Tuning (SFT). In this work, we revisit the traditional Policy Gradient (PG) mechanism and propose a minimalist RL approach termed Group Policy Gradient (GPG). Unlike conventional methods, GPG directly optimize the original RL objective, thus obviating the need for surrogate loss functions. By eliminating the critic and reference models, avoiding KL divergence constraints, and addressing the advantage and gradient estimation bias, our approach significantly simplifies the training process compared to Group Relative Policy Optimization (GRPO). Our approach achieves superior performance without relying on auxiliary techniques or adjustments. As illustrated in the figure below, extensive experiments demonstrate that our method not only reduces computational costs but also consistently outperforms GRPO across various unimodal and multimodal tasks.

Comparison of various RL methods

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Resources

🤗 Models

1. GPG-Open-RS1: The RL model trained on the Open-r1 dataset based on GPG, using DeepSeek-R1-Distill-Qwen-1.5B as the baseline model.
2. GPG-7B: The RL model trained on the simplelr_qwen_level3to5 dataset based on GPG, using Qwen2.5-Math-7B as the baseline model.

Usage

Environment Installation

Clone this repository.

git clone git@github.com:AMAP-ML/GPG.git

cd GPG

Follow the repositories you need and install the required packages.

Experiments on unimodal tasks

Please refer to the training script: ./open-rs/train.sh, ./open-rs/recipes

The results are as follows:

Table: The zero-shot pass@1 performance of the 1.5B models distilled by DeepSeek-R1 across five mathematical reasoning benchmarks. $\dagger$: reproduced results using the released code. $\ddagger$: results from open-rs.

Distilled 1.5B Models	Average	AIME24	MATH-500	AMC23	Minerva	OlympiadBench
DeepSeek-R1-Distill-Qwen-1.5B	48.9	28.8	82.8	62.9	26.5	43.3
Still-3-1.5B-Preview	51.6	32.5	84.4	66.7	29.0	45.4
Open-RS1 $^\dagger$	53.1	33.3	83.8	67.5	29.8	50.9
Open-RS3 $^\dagger$	52.0	26.7	85.4	70.0	27.9	50.2
GPG-RS1	55.7	33.3	87.6	77.5	29.4	50.5
GPG-RS3	55.5	33.3	85.0	80.0	26.8	52.4

Please refer to the training script: ./open-r1/train.sh

Table: The zero-shot pass@1 performance of the 7B models across five mathematical reasoning benchmarks. $\dagger$: reproduced results using the released code. $\ddagger$: results from open-rs. $^\star$: results from Dr.GRPO.

7B Models	Average	AIME24	MATH-500	AMC23	Minerva	OlympiadBench
Qwen-2.5-Math-7B-Instruct $^\ddagger$	43.8	13.3	79.8	50.6	34.6	40.7
Qwen2.5-Math-7B	30.9	13.3	57.6	45.0	14.7	23.7
Qwen2.5-Math-7B (no template) $^\star$	38.2	0.2	69.0	45.8	21.3	34.7
rStar-Math-7B	-	26.7	78.4	47.5	-	47.1
Eurus-2-7B-PRIME	48.9	26.7	79.2	57.8	38.6	42.1
Oat-Zero-7B	51.4	43.3	80.0	62.7	30.1	41.0
Oat-Zero-7B $^\dagger$	47.8	30.0	80.6	55.4	29.0	44.0
OpenReasoner-Zero-7B @ 8k	45.9	13.3	82.4	54.2	31.6	47.9
SimpleRL-Zero-7B $^\star$	46.6	26.7	78.2	60.2	27.6	40.3
GPG-7B	57.7	36.7	84.6	82.5	39.0	45.8

Table: Math reasoning results on Qwen2.5-Math-7B model. $\dagger$: reproduction use the released code.

Models	Average	AIME24	MATH-500	AMC23	Minerva	OlympiadBench
Qwen2.5-Math-7B	30.9	13.3	57.6	45.0	14.7	23.7
GPRO	43.7	16.7	73.4	62.5	30.2	35.7
GPG($F_{norm}=1, \alpha = 1$)	43.9	23.3	76.3	52.5	30.1	37.4
GPG($F_{norm}={std { R(o) } }, \alpha = 1 $)	45.3	23.3	73.6	60.0	30.5	39.3
GPG($F_{norm} =1, \alpha = \frac{B}{B-M}$)	47.8	30.0	75.0	62.5	33.1	38.2
GPG($F_{norm}$=1, $\alpha=\frac{B}{B-M}, \beta_{th}=0.6$)	48.3	30.0	76.2	62.5	34.2	39.0
Dr. GRPO $^\dagger$	43.7	26.7	74.6	50.0	30.1	37.3

Experiments on multimodal tasks

Experiments on VisualThinker-R1-Zero

Please refer to the training script: ./VisualThinker-R1-Zero/src/open-r1-multimodal/run_grpo_SAT.sh

The results are as follows:

Table: Visual reasoning results on CV-Bench, which shows GPG training on base model has overall better performance over GRPO training and the base model.

Models	Total	Count	Relation	Depth	Distance
Qwen2-VL-2B	31.38	54.69	22.46	0.16	31.66
+ SFT	57.84	60.02	68.92	55.00	45.83
+ GRPO	59.47	59.64	66.76	54.16	56.66
+ GPG	76.15	66.62	83.23	81.66	75.50

Experiments on Visual-RFT

Please refer to the training script: ./Visual-RFT/src/scripts/

The results are as follows:

Table: Reasoning grounding results on LISA. GPG surpasses GRPO in reasoning grounding with 239 training images.

Models	mIoUtest	mIoUval	gIoUtest
Qwen2-VL-2B	26.9	30.1	25.3
+ SFT	28.3	29.7	25.3
+ GRPO	37.6	34.4	34.4
+ GPG	51.8	51.3	50.4

Table: 4-shot Results on Four Fine-grained Classification Datasets. GPG shows consistently better results than GRPO on $4$ classification datasets.

Models	Average	Flower102	Pets37	FGVC	Cars196
Qwen2-VL-2B	56.0	54.8	66.4	45.9	56.8
+ SFT	55.6	58.5	55.5	67.9	40.5
+ GRPO	81.9	71.4	86.1	74.8	95.3
+ GPG	89.0	79.3	90.8	88.5	97.5

Experiments on R1-V

Please refer to the training script: ./R1-V/src/scripts/run_grpo_GEOQA_qwen2.5_3b.sh

Table: Geometry reasoning results on GEOQA. GPG is better than GRPO.

Models	GEOQATest
Qwen2.5-VL-3B-Instruct	35.41
+ GRPO	47.48
+ GPG	51.33

Q&A

If you have any questions, please submit an issue or contact huanghailang.hhl<AT>alibaba-inc.com.

Citation

If you find GPG or code useful, please cite

@article{chu2025gpg,
  title={Gpg: A simple and strong reinforcement learning baseline for model reasoning},
  author={Chu, Xiangxiang and Huang, Hailang and Zhang, Xiao and Wei, Fei and Wang, Yong},
  journal={ICLR},
  year={2026}
}

Acknowledgement

We sincerely thank projects open-rs, VisualThinker-R1-Zero, Visual-RFT, R1-V, Open-R1, understand-r1-zero(Dr.GRPO), and Open-r1-multimodal for providing their open-source resources.