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Theorem-SFT improves model reasoning by teaching theorem application

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Theorem-SFT to improve the generalization capabilities of supervised fine-tuned models. This approach shifts the focus from memorizing specific problem-solution pairs to understanding and applying explicit theorems. Theorem-SFT has shown significant performance improvements on mathematical reasoning benchmarks, including notable gains on MATH and GeoQA datasets when applied to LLaMA3.2-3B-Instruct and Qwen2.5-VL-7B-Instruct models. AI

IMPACT Enhances model reasoning by focusing on theorem application, potentially improving performance on complex tasks.

RANK_REASON Academic paper detailing a new method for improving model generalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Theorem-SFT improves model reasoning by teaching theorem application

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Academic paper detailing a new method for improving model generalization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ruiying Peng, Mengyu Yang, Jing Lei, Xiaohui Li, Xueyu Wu, Xinlei Chen ·

    Memorize Theorems, Not Instances: Probing SFT Generalization through Mathematical Reasoning

    arXiv:2605.09270v2 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) is widely used for task-specific adaptation, yet recent work shows it systematically undermines reasoning generalization. We argue the root cause is not memorization itself, but its target: van…

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