RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training

Chemical reaction diagram parsing aims to automaticallyconvert reactions from images into machine-readable formats. Currentleading approaches employ end-to-end generative models to directly de-code reaction diagrams into structured coordinate sequences, performingobject localization and relationship modeling in a unified process. How-ever, two critical bottlenecks remain: severe data scarcity due to thehigh cost of expert annotation, and output instability caused by thecomplex sequential dependencies between chemical reactions, where mi-nor prefix errors can lead to cascading parsing failures. In this paper, wepropose RobustRDP, a Robust Reaction Diagram Parser built on amultimodal large language model (MLLM). Its superior performance pri-marily stems from two advancements: Synthetic-to-Real Data Scal-ing: We develop a layout-driven synthesizer and an efficient annotationplatform to create a large-scale training set, along with a more compre-hensive evaluation benchmark. Robustness-Oriented Training: Wepropose a three-stage progressive training strategy (Pretraining, Multi-Task SFT, DPO). The SFT stage employs two auxiliary tasks includ-ing region-guided reaction parsing and prefix-perturbed reaction parsingto mitigate sequential dependencies between reactions and enhance themodel’s robustness against prior errors. The DPO stage further stabilizesoutputs in failure-prone scenarios. Extensive experiments demonstratethe superior performance of RobustRDP, establishing a solid foundationfor automated reaction diagram parsing. Code and model weights areavailable at https://github.com/jaydetang/RobustRDP.