QED-Nano: Teaching a Tiny Model to Prove Hard Theorems
Proprietary AI systems have recently demonstrated impressive capabilities on complex proof-based problems, with gold-level performance reported at the 2025 International Mathematical Olympiad (IMO). However, the training pipelines behind these systems remain largely undisclosed, and their reliance on large "internal" models and scaffolds makes them expensive to run, difficult to reproduce, and hard to study or improve upon. This raises a central question: can small, open models also be trained to achieve competitive reasoning performance on difficult Olympiad-level math? In this paper, we answer this question by building QED-Nano, a 4B model post-trained for Olympiad-level proofs. Our tra...
| Authors | LM-Provers, Yuxiao Qu, Amrith Setlur et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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