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
arXiv:2609.23367v1 Announce Type: cross
Abstract: FORM is a domain-specific symbolic manipulation language widely used in particle physics for processing the very large algebraic expressions arising...
By Bakar Chargeishvili
The paper introduces Code Consistency Preference Optimization Verification (CCPO), a method that generates computationally sound solutions with dependency graphs to improve execution-consistent preference optimization for language models. By building a scientific reasoning dataset and extracting reasoning steps, prerequisites, and derivability relationships, the authors compute execution consistency scores that are used to fine‑tune models such as Llama‑3‑8B and DeepSeekMath‑7B, achieving significant performance gains on MATH (+17.0%) and GSM8K (+15.1%). The extended Scientific Feasibility Control framework further boosts accuracy on PhyX physics reasoning to 50.1%, surpassing existing models while maintaining high scientific validity and reducing law violations.
By Yunlong Tan, Mingqiao Mo, Hao Zhang
The paper investigates the reliability of rule- and model-based verifiers used in reinforcement learning with verifiable reward (RLVR) for mathematical reasoning. It finds that rule-based verifiers often miss equivalent answers in different formats, causing false negatives that degrade RL performance as models improve. Model-based verifiers achieve higher static accuracy but become vulnerable to reward hacking during RL, misclassifying certain response patterns as correct after fine-tuning.
By Yuzhen Huang, Weihao Zeng, Xingshan Zeng, Qi Zhu, Junxian He
arXiv:2605. 02909v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs).
By Kazuki Egashira, Mark Vero, Jasper Dekoninck, Florian E. Dorner, Robin Staab, Martin Vechev
arXiv:2608. 11573v1 Announce Type: cross Abstract: Achieving effective self-correction, where models verify and correct their own mistakes, remains a fundamental challenge for large language models (LLMs).
By Vu Duc Anh, Nhat M. Hoang, Do Xuan Long, Cong-Duy Nguyen, Ponhvoan Srey, Luu Anh Tuan