ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

• URL: http://arxiv.org/abs/2601.13606v1
• Date: Tue, 20 Jan 2026 05:11:44 GMT
• Title: ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch
• Authors: Zheng Liu, Honglin Lin, Chonghan Qin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Bin Cui, Conghui He, Wentao Zhang, Lijun Wu,
• Abstract summary: We introduce Rollout Posterior Entropy (RPE), a novel metric that quantifies chart complexity.<n>We also develop truth-anchored inverse QA synthesis to guarantee reasoning rigor.<n>To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning.
• Score: 57.01439313241121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to hallucinations and lack the reasoning depth required for complex tasks. To bridge this gap, we propose ChartVerse, a scalable framework designed to synthesize complex charts and reliable reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce Rollout Posterior Entropy (RPE), a novel metric that quantifies chart complexity. Guided by RPE, we develop complexity-aware chart coder to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee reasoning rigor, we develop truth-anchored inverse QA synthesis. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-VL-32B-Thinking.

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