A structured survey of LLM mathematical reasoning that unifies dataset taxonomies, reviews architectures and training strategies, and highlights the gap between answer accuracy and process-level verification.
International Conference on Learning Representations , year=
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2026 2representative citing papers
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.
citing papers explorer
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Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges
A structured survey of LLM mathematical reasoning that unifies dataset taxonomies, reviews architectures and training strategies, and highlights the gap between answer accuracy and process-level verification.
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Can Muon Fine-tune Adam-Pretrained Models?
Constraining fine-tuning updates with LoRA mitigates performance degradation when switching from Adam to Muon on pretrained models.