REChart: Reasoning-Efficient Chart Editing with Large Reasoning Models

• URL: http://arxiv.org/abs/2608.17414v1
• Date: Tue, 18 Aug 2026 06:26:45 GMT
• Title: REChart: Reasoning-Efficient Chart Editing with Large Reasoning Models
• Abstract summary: We introduce REChart, a two-stage training framework that provides process-level supervision over intermediate reasoning steps.<n>First, we synthesize 200k high-quality reasoning trajectories for supervised fine-tuning from a large image-instruction-code pool.<n>Second, we optimize the model via reinforcement learning with two complementary rewards: a emphfidelity reward evaluating code correctness, visual fidelity, and structural consistency, and an emphefficiency reward that assigns each rollout a random thinking budget.
• Score: 6.014442477518621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chart editing requires inferring and modifying visualization code from a reference chart image based on an editing instruction, challenging fine-grained visual reasoning, instruction following, and executable code synthesis capabilities of MLLMs. Large reasoning models (LRMs) with extended Chain-of-Thought (CoT) reasoning are suitable for tackling such complex multimodal tasks. However, our preliminary study reveals an ``inverted-U'' relationship between reasoning length and chart-editing performance: Excessive reasoning often leads to ``overthinking,'' where models drift toward hallucinated visual details or get stuck in redundant reasoning loops. To address the gap, we introduce REChart, a two-stage training framework that provides process-level supervision over intermediate reasoning steps, improving both editing fidelity and reasoning efficiency. First, we synthesize 200k high-quality reasoning trajectories for supervised fine-tuning from a large image-instruction-code pool, using a role-specialized agentic Reason-Score-Refine workflow that iteratively refine the chart code toward higher quality. Second, we optimize the model via reinforcement learning with two complementary rewards: a \emph{fidelity} reward evaluating code correctness, visual fidelity, and structural consistency, and an \emph{efficiency} reward that assigns each rollout a random thinking budget, truncates the reasoning process, and credits the final reasoning segment according to its contribution to the output. On the ChartEdit and ChartMIMIC benchmarks, our model achieves state-of-the-art chart-editing performance among open-source models of comparable scale, while mitigating overthinking and reducing average reasoning token usage by 79.0\% under a maximum thinking budget of 16,384 tokens compared with the base model.

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