Jiahao004/DeepTheorem-qwen-7b-rl

DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural Language and Reinforcement Learning 🚀

Welcome to the GitHub repository for DeepTheorem 🎉, a comprehensive framework for enhancing large language model (LLM) mathematical reasoning through informal, natural language-based theorem proving. This project introduces a novel approach to automated theorem proving (ATP) by leveraging the informal reasoning strengths of LLMs, moving beyond traditional formal proof systems 🌟.

Overview 📖

Theorem proving is a critical benchmark for evaluating complex reasoning in LLMs 🧠. However, formal proof systems often misalign with the informal, natural language knowledge LLMs acquire during pre-training. DeepTheorem addresses this gap by introducing:

1. A Large-Scale Theorem Proving Dataset 📊:

   • Contains high-quality, IMO-level informal theorems and proofs across diverse mathematical domains 📚.
   • Rigorously annotated for correctness, difficulty, and topic categories ✅.
   • Includes systematically constructed verifiable theorem variants for robust evaluation 🔍.

2. RL-Zero 🤖:

   • A novel reinforcement learning strategy tailored for informal theorem proving ⚙️.
   • Utilizes verified theorem variants to incentivize robust mathematical inference 💡.

Deeptheorem demonstrates its superior performance over both opensource and commercial models (i.e Gemini and o1)

Performance 🚀

Deeptheorem achieves the #Rank 5 position along all the commerical models and open source models.

Model	FIMO		HMMT		Putnam		Avg.(#Rank)
	out.	proc.	out.	proc.	out.	proc.	out.	proc.
Gemini2.5-Pro	57.14	54.06	57.63	49.82	64.58	58.75	59.78(#2)	54.21(#3)
o1-mini	60.32	55.23	35.59	30.90	61.46	52.88	52.46(#4)	46.34(#4)
o1	66.67	61.00	47.46	47.30	62.50	57.55	58.88(#3)	55.28(#2)
o3-mini	80.95	77.61	45.76	43.47	78.12	75.12	68.28(#1)	65.40(#1)
*DeepTheorem-RL-7B	55.56	39.07	28.81	20.85	57.29	42.20	47.22(#5)	34.04(#5)
*DeepTheorem-RL-3B	38.10	23.39	25.42	13.56	52.08	33.84	38.53	23.60
*DeepTheorem-RL-1.5B	31.75	15.23	23.73	10.15	52.08	22.79	35.85	16.06

DeepTheorem Dataset 📊

The DeepTheorem dataset comprises 121K IMO-level informal theorems and proofs spanning diverse mathematical domains 🌐. Each theorem-proof pair is rigorously annotated for:

• o3-mini Proofs 🖋️: Ensuring mathematical accuracy through proofs generated or verified by the o3-mini model ✅.
• Truth Value 🔍: The truth value of the theorem extracted from the o3-mini proofs, indicating whether the theorem is true or false ✔️.
• Difficulty 🎚️: Categorized by complexity to suit various LLM capabilities 🧩.
• Topic Categories 🗂️: Covering algebra, geometry, number theory, and more 📘.
• Variants 🔄: Positive or negative variants of the theorem that share the same or inverse truth value of the original theorem 🔀.