VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Haoling Li, Kai Zheng, Jie Wu, Can Xu, Qingfeng Sun, Han Hu, Yujiu Yang
• Venue: arXiv: cs.AI
• Published: 2026-06-22
• Collected: 2026-06-23
• URL: http://arxiv.org/abs/2606.23543v1
• PDF: https://arxiv.org/pdf/2606.23543v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces VeriEvol, a framework that addresses scaling issues in reinforcement learning for visual mathematical reasoning. The framework decouples the difficulty of prompts and the reliability of answers before any policy update, using evolution operators to generate harder prompts and a verifier to ensure answer accuracy. VeriEvol increases the mean accuracy on five visual-math benchmarks, with an additional cumulative +3.88 over an un-evolved RL baseline.

What the Document Says

• The paper presents VeriEvol, a framework for scaling reinforcement learning for visual mathematical reasoning.
• VeriEvol treats scaling as a verifiable data construction problem and decouples prompt difficulty from answer reliability before policy update.
• It uses evolution operators to rewrite low-difficulty image-question seeds into harder prompts.
• HTV-Agent, a verifier, ensures the acceptability of answers by rejecting them if multi-source counter-evidence refutes them.
• The framework is compatible with existing GRPO-style RL recipes.
• Testing on five visual-math benchmarks shows that VeriEvol increases mean accuracy and adds a cumulative +3.88 over an un-evolved RL baseline.

Key Concepts Mentioned

• Reinforcement learning
• Visual mathematical reasoning
• Scalability
• Data pipelines
• Prompt difficulty
• Answer reliability
• Evolution operators
• Verifier
• Counter-evidence
• GRPO
• Benchmark

Claims Made by the Document

• VeriEvol decouples prompt difficulty and answer reliability.
• VeriEvol increases mean accuracy on visual-math benchmarks.
• VeriEvol adds a cumulative +3.88 over an un-evolved RL baseline.

Evidence Used

• The paper describes VeriEvol's components and how they work to decouple prompt difficulty and answer reliability.
• Testing results on visual-math benchmarks are presented, showing increased mean accuracy and the cumulative +3.88 over an un-evolved RL baseline.

Methods / Approach

• Evolution operators
• HTV-Agent verifier

Limitations Stated by the Authors

• The paper does not discuss potential limitations or challenges of VeriEvol beyond the need for further testing and validation.

Limitations Noted by the Agent

Agent interpretation — not from the source.

• It's unclear from the provided text whether VeriEvol is applicable to all types of mathematical reasoning, or if there are any specific conditions or limitations that have not been addressed in the paper.
• The effectiveness of VeriEvol might be influenced by factors such as the quality and diversity of initial data and the complexity of the math problems.

Open Questions

• How effective is VeriEvol for mathematical reasoning tasks outside of visual mathematics?
• What are the potential limitations and challenges of VeriEvol in real-world applications?
• How does the effectiveness of VeriEvol vary with the complexity of math problems?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper focuses on scaling issues for reinforcement learning in visual mathematical reasoning, so it's important to understand that these findings may not apply directly to other types of mathematical reasoning.

Raw Abstract / Excerpt

Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions: as data volume grows, the reward labels themselves must remain reliable. Yet existing data pipelines scale supervision while trusting the labeller, and policy-side methods assume the underlying answers are already correct. We instead treat scaling as a verifiable data-construction problem and decouple two axes before any policy update: prompt difficulty, expanded by route-specific evolution operators, and answer reliability, enforced by offline hypothesis-test falsification. We instantiate this as VeriEvol, an iterative framework with two extensible components: a type-aware evolution module that rewrites low-difficulty image-question seeds into harder, image-grounded prompts; and HTV-Agent, a verifier that accepts an answer only after multi-source counter-evidence has failed to refute it. The resulting verified data scales in volume, extends by adding evolution routes or verifier channels, and plugs directly into existing GRPO-style RL recipes. On a five-benchmark visual-math suite, scaling evolved SFT data from 10K to 250K samples raises the mean accuracy from 35.42 to 54.73; then, with backbone, SFT initialization, and GRPO recipe held fixed, VeriEvol adds a cumulative +3.88 over an un-evolved RL baseline, of which +1.82 comes from evolved prompts and +2.06 from the HTV-Agent verifier. We release the prompts, data, models, code, and the full verifier trace of every sample, so that downstream work can scale and audit the pipeline rather than only inspect its outputs.