Reasoning over mathematical objects: on-policy reward modeling and test time aggregation
#reasoning #mathematical objects #on-policy reward modeling #test time aggregation #AI #mathematical problem-solving #reward modeling #aggregation
📌 Key Takeaways
- The article introduces a method for reasoning over mathematical objects using on-policy reward modeling.
- It emphasizes test time aggregation to enhance reasoning accuracy and reliability.
- The approach aims to improve AI performance in mathematical problem-solving tasks.
- The research combines reward modeling with on-policy learning for better mathematical reasoning.
📖 Full Retelling
arXiv:2603.18886v1 Announce Type: new
Abstract: The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must culminate in formally structured expressions. Yet, current LM evaluations of mathematical and scientific reasoning rely heavily on simplified answer formats such as numerical values or multiple choice options due to the convenience of automated assessment. In this paper we
🏷️ Themes
AI Reasoning, Mathematical Modeling
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Original Source
arXiv:2603.18886v1 Announce Type: new
Abstract: The ability to precisely derive mathematical objects is a core requirement for downstream STEM applications, including mathematics, physics, and chemistry, where reasoning must culminate in formally structured expressions. Yet, current LM evaluations of mathematical and scientific reasoning rely heavily on simplified answer formats such as numerical values or multiple choice options due to the convenience of automated assessment. In this paper we
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