O1 doesn't cheat on math tests - it actually knows how to solve them
A/B testing reveals o1's true mathematical reasoning capabilities beyond memorization
https://arxiv.org/abs/2411.06198
🎯 Original Problem:
OpenAI's Orion-1 (o1) model claims superior reasoning capabilities, but skeptics suggest its performance might stem from memorizing solutions rather than true reasoning abilities.
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🔧 Solution in this Paper:
→ Used A/B testing comparing o1's performance on two datasets: IMO problems (easily accessible) and CNT problems (less accessible but similar difficulty)
→ Implemented a 7-point grading system: 1 point for correct numerical answer, 2 points for intuitive approach, 4 points for detailed reasoning
→ Categorized problems into "search" type (finding specific solutions) and "solve" type (equations/optimization)
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💡 Key Insights:
→ O1 shows strong intuitive reasoning and pattern discovery capabilities
→ Performs exceptionally well on "search" type problems (~70% accuracy)
→ Struggles with rigorous proof steps and "solve" type problems (~21% accuracy)
→ Often uses trial-and-error approach instead of formal proofs
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📊 Results:
→ No significant performance difference between IMO (51.4%) and CNT (48%) datasets
→ T-statistics close to 0, suggesting o1 relies on reasoning rather than memorization
→ Outperforms GPT-4o's benchmark of 39.97% on both datasets
📌 NOTE - This paper is referring to 01-preview model (not the full version 01)
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