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Source note3 min read

VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

Source

arXiv

arxiv.org/abs/2606.23543v1

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Source

Plain-Language Brief

The paper presents VeriEvol, a framework for scaling reinforcement learning in visual mathematical reasoning. It addresses the challenge of maintaining reliable reward labels as data volume grows by treating scaling as a verifiable data construction problem. The framework decouples prompt difficulty and answer reliability, using evolution operators to create harder prompts and a verifier to ensure answers are correct. VeriEvol improves accuracy on a visual-math suite and allows downstream work to scale and audit the pipeline.

What the Document Says

  • The paper introduces VeriEvol, an iterative framework for scaling reinforcement learning in visual mathematical reasoning.
  • VeriEvol decouples prompt difficulty and answer reliability before policy update.
  • The type-aware evolution module rewrites low-difficulty image-question seeds into harder, image-grounded prompts.
  • HTV-Agent is a verifier that accepts an answer only after multi-source counter-evidence has failed to refute it.
  • VeriEvol scales in volume and can be extended by adding evolution routes or verifier channels.
  • The framework plugs directly into existing GRPO-style RL recipes.
  • On a five-benchmark visual-math suite, VeriEvol improves the mean accuracy from 35.42 to 54.73 when evolving SFT data from 10K to 250K samples.
  • VeriEvol adds a cumulative +3.88 over an un-evolved RL baseline, with +1.82 coming from evolved prompts and +2.06 from the HTV-Agent verifier.
  • The authors release the prompts, data, models, code, and the full verifier trace of every sample.

Key Concepts Mentioned

  • VeriEvol
  • reinforcement learning
  • visual mathematical reasoning
  • evolution operators
  • HTV-Agent
  • GRPO-style RL

Claims Made by the Document

  • VeriEvol improves the accuracy on a visual-math suite.
  • Evolution of SFT data from 10K to 250K samples raises the mean accuracy.
  • VeriEvol adds a cumulative +3.88 over an un-evolved RL baseline.

Evidence Used

  • Results on a five-benchmark visual-math suite.

Methods / Approach

  • Type-aware evolution module
  • HTV-Agent

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The model may not be able to understand the technical intricacies of reinforcement learning and the specifics of VeriEvol's implementation.

Open Questions

  • What are the specific evolution routes and verifier channels used in VeriEvol?
  • How does VeriEvol handle edge cases or exceptions during the verification process?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on the framework's approach to scaling reinforcement learning for visual mathematical reasoning but does not provide details about specific evolution routes, verifier channels, or how it handles edge cases.

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.