Fine-Tuning Small Reasoning Models for Quantum Field Theory
📰 ArXiv cs.AI
arXiv:2604.18936v1 Announce Type: cross Abstract: Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability develops while training these models. To investigate this, we perform the first academic fine-tuning study of small (7B-parameter) reasoning models dedicated specifically to theoretical physics. Because open-source verifiable training data required to train such capabilitie
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Title: Fine-Tuning Small Reasoning Models for Quantum Field Theory
Abstract:
arXiv:2604.18936v1 Announce Type: cross Abstract: Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability develops while training these models. To investigate this, we perform the first academic fine-tuning study of small (7B-parameter) reasoning models dedicated specifically to theoretical physics. Because open-source verifiable training data required to train such capabilitie
Abstract:
arXiv:2604.18936v1 Announce Type: cross Abstract: Despite the growing application of Large Language Models (LLMs) to theoretical physics, there is little academic exploration into how domain-specific physics reasoning ability develops while training these models. To investigate this, we perform the first academic fine-tuning study of small (7B-parameter) reasoning models dedicated specifically to theoretical physics. Because open-source verifiable training data required to train such capabilitie
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