MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction

Abstract

Multimodal Large Language Models (MLLMs) have recently demonstrated promising capabilities in multimodal coding tasks such as chart-to-code generation. However, existing methods primarily rely on supervised fine-tuning (SFT), which requires the model to learn code patterns through chart-code pairs but does not expose the model to a code execution environment. Moreover, while self-correction through execution feedback offers a potential route to improve coding quality, even state-of-the-art MLLMs have been shown to struggle with effective self-correction. In this work, we introduce MM-ReCoder, a chart-to-code generation model trained with reinforcement learning (RL) and equipped with self-correction ability. We propose a two-stage multi-turn self-correction RL strategy based on Group Relative Policy Optimization (GRPO). The first stage enhances the model's self-correction ability via rolling out a shared first turn, while the second stage improves the coding capability with full-trajectory optimization. MM-ReCoder learns to produce more accurate and executable code through the interaction with the environment and by iteratively correcting its own outputs. Our results on three chart-to-code benchmarks demonstrate the state-of-the-art performance of MM-ReCoder.

Figures (13)

• Figure 1: Training pipeline of MM-ReCoder. We first conduct two stages of cold start: (a) we first train the model on ground truth chart-code pairs with SFT, then (b) we construct self-correction data with Qwen3VL-235B qwen3vl_blog, filter the successful ones, and train our model on the filtered data. After cold start, we conduct two stages of reinforcement learning: (c) we first enhance the model's self-correction capability in the second turn via shared-first-turn optimization, then (d) we optimize the two turns jointly to improve the coding ability.
• Figure 2: Result of a model trained solely with rule-based reward. The model receives a full rule-based reward though the texts are overlapped. But the model-based reward can penalize this chart.
• Figure 3: Qualitative results comparing ground truth, $1^{st}$-round generation, and $2^{nd}$-round self-correction (left to right).
• Figure A1: Training curves of MM-ReCoder. Curves on the training set are smoothed with a window size of 20 steps. We evaluate the model on ChartMimic every 40 steps. Note that the value of the rule-based reward can be slightly different from the reported model performance on low-level score because the latter is evaluated under the official evaluation codebase of ChartMimic.
• Figure A2: Qualitative result of MM-ReCoder for self-correction. The model removes the text labels from the pie chart in the second turn.