Researchers have developed a new framework to improve physics reasoning in large language models by explicitly encoding the physical modeling process. This approach involves a two-stage post-training strategy: supervised fine-tuning for structured modeling and reinforcement learning with rubric-based feedback for quality improvement. Experiments on several multimodal physics benchmarks demonstrated consistent performance gains across different models and datasets, with the physical modeling output outperforming GRPO by approximately 3% on PhysReason, PhyX, and SeePhys benchmarks. AI
IMPACT This research could lead to more capable LLMs for scientific and engineering domains by improving their ability to model and solve complex physics problems.
RANK_REASON The cluster contains a research paper detailing a new framework for physics reasoning in LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- DagsHub
- Gotit.pub
- GRPO
- Hugging Face
- IArxiv
- PhysReason
- Phyxelida
- ScienceCast
- SeePhys
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