arXiv:2605.09270v2 Announce Type: replace 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: vanilla SFT drives models to exploit and memorize spurious surface correlations in problem-solution pairs, leaving them brittle to superficial input variations. To address this, we propose Theorem-SFT, which reorients supervision toward explicit theorem application by teaching models how rules are invoked rather than what answe
The proliferation of SFT models has exposed limitations in their reasoning generalization, necessitating immediate advancements in training methodologies.
Improving SFT generalization in mathematical reasoning is critical for developing more robust and reliable AI systems capable of complex problem-solving.
This research shifts the focus of AI training from memorizing answers to understanding and applying underlying principles, potentially leading to more adaptable models.
- · AI researchers
- · Developers of AI agents
- · Sectors requiring complex AI reasoning
- · Educational AI platforms
- · AI models reliant on superficial pattern matching
- · Companies offering brittle AI reasoning solutions
AI models will become more capable of abstract reasoning and less susceptible to minor input variations.
This improved reasoning could accelerate scientific discovery and automate more complex cognitive tasks.
A fundamental shift in AI's intellectual capabilities could lead to new forms of human-AI collaboration and agentic systems.
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Read at arXiv cs.LG