arXiv AI By Akshay Dhalpe, Arnab Chattopadhyay, Shahriar Bakrani Balani, Eric Cortan\'ea, Sasu Tarkoma

pi-Net: Empirical Latent Representations for Differentiable Physics Heads in Data-Scarce Systems

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arXiv Computation and Language
Aug 25

Decoupled Physical Modeling and Execution for Physics Reasoning

The paper introduces a framework that separates physical modeling from execution in physics reasoning tasks. It uses a two‑stage post‑training approach: supervised fine‑tuning to build structured models and reinforcement learning with rubric‑based feedback to refine them. Experiments on PhysReason, PhyX, and SeePhys show that this explicit modeling improves reasoning performance by about 3% on average for small LLMs.

By Ye Zhang, Xuehang Guo, Rui Pan, Pengfei Yu, Denghui Zhang, Manling Li, Qingyun Wang