Towards an AI Musician: Synthesizing Sheet Music Problems for Musical Reasoning

Enhancing the ability of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to interpret sheet music is a crucial step toward building AI musicians. However, current research lacks both evaluation benchmarks and training data for sheet music reasoning. Inspired by mathematics, where simple operations yield infinite verifiable problems, we introduce a novel approach that treats core music theory rules, such as those governing beats and intervals, as programmatic functions to systematically synthesize a vast and diverse corpus of sheet music reasoning problems. This approach allows us to introduce a data synthesis framework that generates verifiable sheet music questions in both textual and visual modalities, leading to the Synthetic Sheet Music Reasoning Benchmark (SSMR-Bench) and a complementary training set. Evaluation results on SSMR-Bench highlight the key role reasoning plays in interpreting sheet music, while also pointing out the ongoing challenges in understanding sheet music in a visual format. By leveraging synthetic data for RL VR, all models show significant improvements on the SSMR-Bench. Additionally, they also demonstrate considerable advancements on previously established human-crafted benchmarks, such as MusicTheoryBench and the music subset of MMMU. Finally, our results show that the enhanced reasoning ability can also facilitate music composition. "The sheet music is the language of musicians." Recent advancements in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have inspired researchers to explore the potential of developing AI musicians (Qu et al., 2025; Bradshaw & Colton, 2025; Wang et al., 2024). Given that sheet music is the universal language of musicians, the ability to read and interpret it is an essential step for AI musicians (Y uan et al., 2024; Wang et al., 2025). As illustrated in Figure 1, sheet music reasoning differs fundamentally from Music Knowledge QA (Li et al., 2024), which evaluates memorized knowledge, and from sheet music recognition (Chen et al., 2025a), which focuses on identifying notation from images.