Group Relative Policy Optimization (GRPO) has emerged as the de facto Reinforcement Learning (RL) objective driving recent advancements in Multimodal Large Language Models. However, extending this success to open-source multimodal generalist models remains heavily constrained by two primary challenges: the extreme variance in reward topologies across diverse visual tasks, and the inherent difficulty of balancing fine-grained perception with multi-step reasoning capabilities. To address these issues, we introduce Gaussian GRPO (G2RPO), a novel RL training objective that replaces standard linear scaling with non-linear distributional matching. By mathematically forcing the advantage distribution of any given task to strictly converge to a standard normal distribution, N(0,1), G2RPO theoretically ensures inter-task gradient equity, mitigates vulnerabilities to heavy-tail outliers, and offers symmetric update for positive and negative rewards. Leveraging the enhanced training stability provided by G2RPO, we introduce two task-level shaping mechanisms to seamlessly balance perception and reasoning. First, response length shaping dynamically elicits extended reasoning chains for complex queries while enforce direct outputs to bolster visual grounding. Second, entropy shaping tightly bounds the model's exploration zone, effectively preventing both entropy collapse and entropy explosion. Integrating these methodologies, we present OpenVLThinkerV2, a highly robust, general-purpose multimodal model. Extensive evaluations across 18 diverse benchmarks demonstrate its superior performance over strong open-source and leading proprietary frontier models.
Figures & tables
Figure 1: Performance improvement (relative) of OpenVLThinkerV2 over its baseline Qwen3-VL-Instruct-8B across diverse visual tasks.
Figure 3: Comparison of response length dynamics during training. G 2 RPO effectively encourage early convergence. a) It scales up reasoning length for complex question. b) It reduces overthinking for visual-centric tasks, enhancing perceptual grounding and mitigating hallucinations. c) For both reasoning and perception heavy tasks, the generation length stabilizes within an optimal range, effectively balancing both capabilities.
pi=Nrank(Ri)−0.5
(6)
Algorithm 1 G 2 RPO : Distributional Matching via 1D Optimal Transport
Figure 4: Effect of task-level entropy shaping. G 2 RPO effectively prevents entropy explosion for reasoning-centric tasks and OOD task (spatial reasoning) while concurrently mitigating entropy collapse in vision-centric tasks.
General VQA
Math VQA
Chart VQA
Model
MMMU
MMBench
MMStar
MathVista
MathVerse
MathVision
AI2D
ChartQA
CharXiv(RQ)
GPT-4o ( Hurst et al., 2024 )
70.7
84.3
65.1
63.8
41.2
30.4
84.9
86.7
47.1
Gemini 2.5 Pro ( Comanici et al., 2025 )
81.7
90.1
77.5
82.7
82.9
73.3
88.4
83.3
67.9
MM-Eureka-7B ( Meng et al., 2025a )
57.3
87.7
64.4
73.0
50.3
26.9
84.1
77.3
39.5
OpenVLThinker-7B ( Deng et al., 2025 )
55.8
87.8
59.1
70.2
47.9
25.3
81.8
85.7
39.3
VL-Rethinker-7B ( Wang et al., 2025b )
56.7
87.9
62.7
74.9
54.2
32.3
80.8
84.7
39.8
Table 1: Performance comparison of various models across visual reasoning benchmarks. We report our reproduced results for Qwen3-VL-Instruct-8B under the same setting. Our model consistently outperforms strong open-source baselines.
Table 2: Results on (a) document understanding and (b) spatial reasoning benchmarks. We compare against both general multimodal models and task-specific expert baselines.
Model
RefCOCO
RefCOCO+
RefCOCOg
Gemini 1.5 Pro ( Gemini Team, 2024 )
73.2
62.5
75.2
Grounding DINO ( Liu et al., 2023 )
90.6
88.2
86.1
VLM-R1 ( Shen et al., 2025 )
90.5
84.3
87.1
DeepEyes ( Zheng et al., 2025b )
89.8
83.6
86.7
OneThinker-8B ( Feng et al., 2025b )
92.0
87.0
89.2
Qwen3-VL-Instruct
89.9
84.5
86.8
Table 3: Evaluation results for Grounding task. We report scores on val splits. Our model consistently outperforms previous baselines.
Model
General VQA
Math VQA
Chart VQA
Grounding
Document Understanding
Spatial Reasoning
Qwen3-VL-Instruct-8B
71.3
59.2
69.9
87.1
86.8
60.9
Qwen3-VL + G 2 RPO
76.9
64.8
74.5
90.2
90.6
62.3
+ task-level entropy loss
77.0
65.1
75.3
90.4
90.8
62.8
+ task-level length reward
77.4
65.7
75.4
90.5
91.1
63.2
OpenVLThinkerV2 (Ours)
77.9
66.2
76.0
90.7
91.4
63.6
Table 4: Ablation study evaluating the impact of different training components across the six main domains. Scores represent the average performance within each task category.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: Average Accuracy Reward Comparison across all tasks on the Validation set during Training. G 2 RPO demonstrates stable and superior performance overall.
Figure 6: Average Length Reward Comparison across all tasks on the Validation set during Training. G 2 RPO demonstrates stable and superior performance overall.
Figure 7: Average Format Reward Comparison across all tasks on the Validation set during Training. G 2 RPO demonstrates stable and superior performance overall.
Figure 8: Average Structure Reward Comparison across all tasks on the Validation set during Training. G 2 RPO demonstrates stable and superior performance overall.
School of Computing & Data Science, The University of Hong Kong · School of Computing & Data Science and Institute of Data Science, The University of Hong Kong