Chem-R: Learning to Reason as a Chemist
Authors: name: Weida Wang, name: Benteng Chen, name: Di Zhang, name: Wanhao Liu, name: Shuchen Pu, name: Ben Gao, name: Jin Zeng, name: Lei Bai, name: Wanli Ouyang, name: Xiaoyong Wei, name: Tianshu Yu, name: Tianfan Fu, name: Shuzhou Sun, name: Jiatong Li, name: Zifu Wang, name: Yuqiang Li, name: Shufei Zhang
Organization: shanghai ailab 

Although large language models (LLMs) have significant potential to advance
chemical discovery, current LLMs lack core chemical knowledge, produce
unreliable reasoning trajectories, and exhibit suboptimal performance across
diverse chemical tasks. To address these challenges, we propose Chem-R, a
generalizable Chemical Reasoning model designed to emulate the deliberative
processes of chemists. Chem-R is trained through a three-phase framework that
progressively builds advanced reasoning capabilities, including: 1) Chemical
Foundation Training, which establishes core chemical knowledge. 2) Chemical
Reasoning Protocol Distillation, incorporating structured, expert-like
reasoning traces to guide systematic and reliable problem solving. 3)
Multi-task Group Relative Policy Optimization that optimizes the model for
balanced performance across diverse molecular- and reaction-level tasks. This
structured pipeline enables Chem-R to achieve state-of-the-art performance on
comprehensive benchmarks, surpassing leading large language models, including
Gemini-2.5-Pro and DeepSeek-R1, by up to 46% on molecular tasks and 66% on
reaction tasks. Meanwhile, Chem-R also consistently outperforms the existing
chemical foundation models across both molecular and reaction level tasks.
These results highlight Chem-R's robust generalization, interpretability, and
potential as a foundation for next-generation AI-driven chemical discovery.