Vero: An Open RL Recipe for General Visual Reasoning

Gabriel Sarch; Linrong Cai; Qunzhong Wang; Haoyang Wu; Danqi Chen; Zhuang Liu

cs.CVApr 6, 2026

Vero: An Open RL Recipe for General Visual Reasoning

Authors: Gabriel Sarch, Linrong Cai, Qunzhong Wang, Haoyang Wu, Danqi Chen, Zhuang Liu

Organizations: Princeton University

Abstract

What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) suggest that broad visual reasoning is within reach, yet their closed data and reinforcement learning (RL) pipelines make their gains difficult to study, reproduce, or extend. We introduce Vero, a family of fully open VLMs that match or exceed existing open-weight models across diverse visual reasoning tasks. We scale RL data and rewards across six broad task categories, constructing Vero-600K, a 600K-sample dataset from 59 datasets, and designing task-routed rewards that handle heterogeneous answers. Across VeroEval, our 30-benchmark suite, Vero-600K outperforms existing RL datasets under controlled comparisons. Applied to five starting models, Vero variants gain 2.9-5.4 points on average over their initial models. Notably, Vero-Qwen3I-8B, trained on the Instruct model, surpasses Qwen3-VL-8B-Thinking by 3.8 points on average without additional distillation. Systematic ablations reveal that different task categories elicit distinct reasoning patterns and that broad gains depend on learning them jointly rather than in isolation. All data, code, and models are publicly available.

Figures & tables

Figure 1: Vero achieves state-of-the-art performance across six task categories using a fully open RL recipe. Top left: Training curves versus RL FLOPs for Vero-600K compared with existing open RL datasets, all finetuned from Qwen2.5-VL-7B-Instruct; dashed lines indicate training beyond one epoch. Reported is mean

\pm

sem (3 seeds). Top center: Summary of the broad task categories targeted in Vero-600K and VeroEval . Top right: Overall performance of Vero (Avg@5) against each initial RL model across the 30 VeroEval benchmarks. Bottom row: Same as top right, but displaying per-category scores for Vero trained on Qwen3-VL-8B-Instruct and Qwen3.5-9B.

Figure 2: Composition of Vero-600K . The inner ring shows six task categories, each allocated 100K samples (600K total), and the outer ring shows their 59 constituent datasets. The categories represent real-world use cases and cover distinct visual reasoning capabilities (Sections 5 and 7 ). Categories are uniformly sampled to balance learning across tasks.

Figure 3: Vero-600K data curation pipeline. Starting from over 250 candidate datasets, we assign each to one of six task categories and apply multi-stage selection and filtering: heuristic screening (size, resolution, answer format), manual quality control, LLM-based question filtering for ambiguity and verifiability, and answer filtering for stable reward computation. The retained data are combined into a uniformly weighted mixture across task categories and used for on-policy RL training with task-routed rewards.

Figure 4: Examples from each task category illustrate the breadth of Vero-600K . We show representative samples of our training data from the six categories, highlighting the diversity of visual inputs, question formats, and answer types covered by our training set.

Table 5

Figure 5: Accuracy verifiers for our multi-task reward. Each card illustrates a verifier used in Vero , with an example visual question, ground-truth answer, and model prediction. Verifiers include binary rewards (string match, multiple choice (not displayed), list string match (not displayed), numeric, ordering, point-in-box) and graded rewards (IoU/F1 for grounding, field match for web actions, rule-based checks for instruction following, and LLM-as-judge for open-ended responses). This task-routed design enables accurate reward computation across diverse answer formats.

Figure 6: Per-category RL training curves. Evaluation score vs. RL FLOPs for RL runs using Vero-600K and three prior open RL datasets, all starting from Qwen2.5-VL-7B-Instruct under identical RL settings. Curves report mean and SEM across seeds where shown. Dashed segments indicate training beyond one epoch. Vero-600K leads on five of six categories throughout training; on STEM it remains within 2 points of the best prior dataset.

Figure 7: Diverse task mixing eliminates negative cross-task transfer. Each row shows a model trained on a single task category (or mixture of all categories). Values are absolute score changes relative to the base model. Single-task training yields selective transfer, while mixing achieves consistent gains.

Figure 10: Performance improves with greater training data exposure. We plot benchmark performance as each model sees progressively more samples from the fixed 600K RL training mixture over the course of a single training pass.

Figure 11: Single-task training elicits a range of behavioral profiles, many of which are distinct. Average behavior presence rates for Vero on Qwen3-VL-8B-Instruct, aggregated over six validation task categories after balanced subsampling. 34 high-level cognitive behaviors, sorted by overall prevalence. Single-task training elicits distinct high-level strategies, and these differences carry through to the skill level.

Figure 12: Task category skills are largely distinct. Each bar shows the predicted classification distribution (as proportions) from a logistic regression probe trained on skill embeddings, with the values indicating per-category accuracy. The probe achieves 0.77 overall accuracy, with STEM (0.84) and Chart & OCR (0.82) yielding the most distinctive skills. Knowledge & Recognition is the least separable (0.59), with notable confusion toward Grounding, Counting & Search, reflecting shared visual grounding operations across these categories.

Figure 13: Single-task training elicits distinct behavioral skills. Word clouds of extracted low-level skills reveal that models develop highly specialized strategies adapted to their specific training domains. For instance, STEM training fosters formula-driven logic (e.g., “Apply Arc Length Formula”); Grounding models prioritize localized perception (e.g., “Determine Relative Position”).

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Ret.	Answer type	Reward type(s)
Grounding, Counting & Search
AerialVG ( Liu et al., 2025a )	12,634	bbox coordinates	grounding
GroundUI ( Zheng et al., 2025b )	12,064	click coordinates	clicking
MultiHop ( Li et al., 2026 )	6,316	integer count	counting
Objects365-QA ( Shao et al., 2019 )	12,632	bbox coordinates	grounding
OOD-VQA ( Shi and Lee, 2024 )	5,028	integer count	counting

Appendix

Table A1 : Retained training datasets used in our RL mixture. Retained sizes are taken from the composition source used to generate the training-data figure. Captioning and instruction-following retained sizes are rounded display values.

Benchmark	Description
Chart & OCR
ChartQA-Pro ( Masry et al., 2025 )	diverse chart question answering
ChartQA ( Masry et al., 2022 )	chart reasoning and question answering
InfoVQA ( Mathew et al., 2022 )	infographic question answering
CharXiv ( Wang et al., 2024b )	scientific chart understanding
ChartMuseum ( Tang et al., 2025 )	chart visual reasoning

Appendix

Table A2 : Evaluation benchmarks in VeroEval , organized by task category. The right column gives a short description of what each benchmark evaluates.

Reason	Description	Example
Single-ground-truth datasets
Multi-value answers ( ${\sim}300$ )	Ground truth contains multiple distinct values that cannot be reduced to a single verifiable target.	AC = 4, BD = 4
Ambiguous text labels ( ${\sim}300$ )	Descriptive phrases requiring fuzzy or semantic matching beyond exact string comparison.	“Isosceles triangle”
Unsupported notation ( ${\sim}300$ )	Scientific notation or symbolic algebraic expressions outside our numeric parser’s scope.	$b=a\cos C$
Empty / invalid GT	Ground truth is missing, empty, or malformed, making reward computation impossible.	“Empty case”
Unit mismatch	Unit is inconsistent with the question context or cannot be cleanly stripped to a numeric value.	Mass answered in cm

Appendix

Table A3: Common reasons for answer filtering. The top section covers single-ground-truth datasets (frequency estimates from a manual sample); the bottom section covers multi-annotator datasets.

Hyperparameter	Value
Framework	VeRL
FSDP strategy	fsdp2
Rollouts per prompt ( $G$ )	8
Train batch size	256
PPO mini-batch size	128
Learning rate	$1\times 10^{-6}$

Appendix

Table B1: RL training hyperparameters for all Vero models. Training uses VeRL with GSPO ( Zheng et al., 2025a ) , asymmetric clipping (

\varepsilon_{\text{low}}<\varepsilon_{\text{high}}

), and no KL penalty to allow less-restricted policy updates.

Base model	GPUs	Steps	Ctx.	Max px.	Dtype	Coords
MiMo-VL-7B-SFT	8 $\times$ H100	2,000	28,672	$3072^{2}$	bf16	absolute
Qwen2.5-VL-7B-Inst.	8 $\times$ H100	2,000	24,576	$3072^{2}$	fp16	absolute
Qwen3-VL-8B-Inst.	8 $\times$ H200	2,000	36,864	$4096^{2}$	fp16	norm. 0–1k
Qwen3-VL-8B-Think.	8 $\times$ H200	2,000	36,864	$4096^{2}$	fp16	norm. 0–1k

Appendix

Table B2: Base-model training configurations for the Vero variants, including context length, maximum image resolution, precision, and coordinate format. Qwen-family models use fp16 for improved training stability ( Qi et al., 2025 ) .

Table 19

Figure D4 : Behavioral skill analysis presence rates for models trained on individual task categories and one trained on all categories (mix all). Training on individual task categories impacts the emergence of fine-grained skills within each domain, with green indicating higher skill presence and red indicating lower. For instance, chart skills center on reading operations like "Axis Analysis", while spatial skills center on physical state reasoning like "Mental Simulation".

Skill	Definition
Captioning & Instruction Following
Adjective Selection For Visual Description	Choosing descriptive adjectives that reflect style and function to capture a scene.
Adopt Perspective	Use vivid language to convey a specific perspective without using pronouns.
Analyze Atmosphere	Establish emotional connection to a setting via environmental interaction.
Analyze Contrasts	Use visual contrast (color, structure) to highlight key information.
Analyze Visual Composition	Assess visual elements like focal points to determine mood or message.

Appendix

Table D2 : Summary of skill definitions. A comprehensive glossary providing operational definitions for the fine-grained skills evaluated across all six task categories.

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Md Tanvirul Alam

Rochester Institute of Technology Rochester, NY, USA

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May 25, 2026

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Recent multimodal large language models (MLLMs) achieve strong performance on visual reasoning benchmarks, yet it remains unclear to what extent such performance reflects reasoning directly grounded in visual evidence. We introduce VisReason, a benchmark for vision-centric reasoning in everyday scenarios where perception and inference are tightly coupled. VisReason contains 1,505 questions across 10 categories spanning perceptual, structural, and conceptual reasoning. Our evaluation shows that VisReason poses a qualitatively different challenge from existing benchmarks, exposing substantial gaps between humans and current MLLMs and revealing limited benefits from test-time reasoning strategies. VisReason offers a focused diagnostic for evaluating vision-centric reasoning beyond language.

Longteng Guo, Yifan Wang, Pengkang Huo +4

Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences

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VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning

Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs). Yet, its potential in multimodal large language models (MLLMs) remains largely untapped, hindered by the absence of large-scale datasets that capture the rich, spatially grounded reasoning intrinsic to visual understanding. Existing visual-CoT resources are typically small, domain-specific, or lack the human-like stepwise structure necessary for compositional visual reasoning. In this paper, we introduce VisReason, a large-scale dataset designed to advance visual Chain-of-Thought reasoning. VisReason comprises 489K annotated examples spanning four diverse domains, each featuring multi-round, human-like rationales that guide MLLMs through interpretable visual reasoning steps. Building upon this, we curate VisReason-Pro, a 165K subset produced with a stronger expert-level GPT annotator, enriched with detailed reasoning traces and 3D spatial grounding via depth-informed annotations. Fine-tuning the state-of-the-art Qwen2.5-VL model on VisReason and VisReason-Pro yields substantial improvements in step-by-step visual reasoning accuracy, interpretability, and cross-benchmark generalization. These results demonstrate that VisReason equips MLLMs with more systematic and generalizable reasoning capabilities. We envision VisReason as a cornerstone for cultivating human-like visual reasoning, paving the way toward the next generation of multimodal intelligence.

Lingxiao Li, Yifan Wang, Xinyan Gao +3

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Visual Spatial Reasoning Visual Reasoning Multimodal Large Language Models Multimodal CoT Reasoning Visually Grounded Reasoning Visual CoT

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Abstract

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