Chem-R: Learning to Reason as a Chemist
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.
68f839d57669bcaeecce0cbe
Weida Wang
claimed_verified
2025-10-22T15:43:56.271Z
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Weida Wang
user
weidawang
68f839d57669bcaeecce0cbf
Benteng Chen
68f839d57669bcaeecce0cc0
Di Zhang
68f839d57669bcaeecce0cc1
Wanhao Liu
68f839d57669bcaeecce0cc2
Shuchen Pu
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2025-10-22T15:43:45.089Z
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pushuchen
user
komusama0930
68f839d57669bcaeecce0cc3
Ben Gao
68f839d57669bcaeecce0cc4
Jin Zeng
68f839d57669bcaeecce0cc5
Lei Bai
68f839d57669bcaeecce0cc6
Wanli Ouyang
68f839d57669bcaeecce0cc7
Xiaoyong Wei
68f839d57669bcaeecce0cc8
Tianshu Yu
68f839d57669bcaeecce0cc9
Tianfan Fu
68f839d57669bcaeecce0cca
Shuzhou Sun
68f839d57669bcaeecce0ccb
Jiatong Li
68f839d57669bcaeecce0ccc
Zifu Wang
68f839d57669bcaeecce0ccd
Yuqiang Li
68f839d57669bcaeecce0cce
Shufei Zhang
Chem-R: Learning to Reason as a Chemist
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.
Chem-R, a three-phase trained Chemical Reasoning model, achieves superior performance on chemical tasks by integrating core knowledge, expert reasoning, and multi-task optimization.
Chem-R Chemical Foundation Training Chemical Reasoning Protocol Distillation Multi-task Group Relative Policy Optimization state-of-the-art performance molecular tasks reaction tasks Gemini-2.5-Pro DeepSeek-R1 robust generalization interpretability