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

• URL: http://arxiv.org/abs/2604.01600v1
• Date: Thu, 02 Apr 2026 04:21:09 GMT
• Title: MM-ReCoder: Advancing Chart-to-Code Generation with Reinforcement Learning and Self-Correction
• Abstract summary: We introduce MM-ReCoder, a chart-to-code generation model trained with reinforcement learning (RL)<n>We propose a two-stage multi-turn self-correction RL strategy based on Group Relative Policy Optimization (GRPO)<n>Our results on three chart-to-code benchmarks demonstrate the state-of-the-art performance of MM-ReCoder.
• Score: 18.226927359110018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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