Test-time Compute Search

Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models

Kimina-Prover is a new large formal reasoning model that combines reinforcement learning with test-time search to improve mathematical theorem proving. The approach applies RL-trained search strategies at inference time, targeting formal proof generation in systems like Lean. The work is published via the AI-MO (AI for Math Olympiad) team on Hugging Face, continuing the trend of applying RL and extended compute at test time to hard reasoning tasks.