VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

• URL: http://arxiv.org/abs/2606.23543v1
• Date: Mon, 22 Jun 2026 16:17:30 GMT
• Title: VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
• Abstract summary: Scaling reinforcement learning for visual mathematical reasoning requires more than generating harder questions.<n>We instantiate this as VeriEvol, an iterative framework with two components.<n>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.
• Score: 71.22358682581215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ProMSA:Progressive Multimodal Search Agents for Knowledge-Based Visual Question Answering [26.55005240500685]
  ProMSA is a progressive multimodal search agent for Knowledge-based Visual Question Answering (KB-VQA)<n>Given an image-question pair, the agent iteratively chooses image search, text search, or stop, under explicit tool-call budgets and with deduplication to avoid redundant retrieval.<n>Experiments on E-VQA and InfoSeek show consistent gains over strong RAG and agent baselines, and improved retrieval and end-to-end accuracy.
  arXiv  Detail & Related papers  (2026-06-26T11:23:18Z)
• REVES: REvision and VErification--Augmented Training for Test-Time Scaling [53.197756110943395]
  We propose a two-stage iterative framework that alternates between online data/prompt augmentation and policy optimization.<n>We observe gains of +6.5 points over the RL baseline and +4.0 points over standard multi-turn training.
  arXiv  Detail & Related papers  (2026-06-17T10:37:23Z)
• AuditWeave: A Tamper-Evident, Auditor-Navigable Evidence Layer for AI-Assisted and Data-Transformation Workflows [0.0]
  AuditWeave is a lightweight Python library that records the steps of AI-assisted and data-transformation into a single hash-chained ledger.<n>We describe the design and evaluate recording overhead, scalability, and tamper-detection correctness on the reference implementation.
  arXiv  Detail & Related papers  (2026-06-14T21:46:04Z)
• VeriTrace: Evolving Mental Models for Deep Research Agents [43.43659414477917]
  Deep research agents face vast, interdependent, and pervasively uncertain information.<n>Existing systems explore what evolving intermediate representations should look like, but leave their evolution to the implicit reasoning.<n>We argue that an agent's mental model should instead evolve through explicit feedback that continuously aligns task understanding with reality.
  arXiv  Detail & Related papers  (2026-05-25T17:46:57Z)
• Trace2Skill: Verifier-Guided Skill Evolution for Long-Context EDA Agents [0.3733676450456031]
  We present Trace2Skill, a test-time scaling framework that improves a hardware agent without RTL-specialized model fine-tuning.<n>Rather than training a new model or only sampling more candidate solutions, Trace2Skill treats the agent's natural-language skill as an evolvable policy.<n>It mines repeated rollout traces for success and failure modes, converts them into dense diagnostics and oracle lessons, and uses an oracle, mutator, and selector loop to produce task-specific skills.
  arXiv  Detail & Related papers  (2026-05-20T23:10:49Z)
• GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL [64.8155693023222]
  Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.<n>This gap stems from a shortage of high-quality, action-aligned reasoning data.<n>We present GUI-Libra, a tailored training recipe that addresses these challenges.
  arXiv  Detail & Related papers  (2026-02-25T18:34:57Z)
• Pull Requests as a Training Signal for Repo-Level Code Editing [49.82435173554125]
  Clean Pull Request (Clean-PR) is a mid-training paradigm that leverages real-world GitHub pull requests as a training signal for repository-level editing.<n>We introduce a scalable pipeline that converts noisy pull request diffs into Search/Replace edit blocks through reconstruction and validation.<n>On SWE-bench, our model significantly outperforms the instruction-tuned baseline, achieving absolute improvements of 13.6% on SWE-bench Lite and 12.3% on SWE-bench Verified.
  arXiv  Detail & Related papers  (2026-02-07T09:22:25Z)
• Scaling the Scaling Logic: Agentic Meta-Synthesis of Logic Reasoning [18.75349680577575]
  SSLogic is a framework for continuous family evolution with controllable difficulty.<n>Training on SSLogic-evolved data yields consistent gains over the seed baseline at matched steps.
  arXiv  Detail & Related papers  (2026-01-23T13:26:01Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• Inverse Scaling: When Bigger Isn't Better [80.42834197416444]
  Large language models (LMs) show predictable improvements to overall loss with increased scale. We present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale.
  arXiv  Detail & Related papers  (2023-06-15T20:11:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.