Lai Jiang

(1/4)🚨 Introducing Goedel-Prover V2 🚨 🔥🔥🔥 The strongest open-source theorem prover to date. 🥇 #1 on PutnamBench: Solves 64 problems—with far less compute. 🧠 New SOTA on MiniF2F: * 32B model hits 90.4% at Pass@32, beating DeepSeek-Prover-V2-671B’s 82.4%. * 8B > 671B: Our 8B model matches DeepSeek-671B on MiniF2F. 📚 Leading on MathOlympiadBench (IMO-level problems) * Solves 73 vs 50 over 671B DeepSeek Prover 🔓 Website: blog.goedel-prover.com 🔓 Model 32B: huggingface.co/Goedel-LM/Goed… 🔓 Model 8B huggingface.co/Goedel-LM/Goed… 🔓Data and training pipeline will be released soon. Amazing Collaborators: @sangertang1999 @Lyubh22 @__zrrr__ @juihuichung @thomaszhao1998 @pero733858111 @thiiis_user @EmilyJge @JingruoS5931 @wujiayun12 @GesiJiri68334 @davidjesusacu @KaiyuYang4 @hongzhou__lin @YejinChoinka @danqi_chen @prfsanjeevarora @chijinML

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: yu-fangxu.github.io/FoR.github.io/ Paper: arxiv.org/abs/2406.05673 Github: github.com/Yu-Fangxu/FoR