"AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling"

AceMath introduces a suite of math-specialized models that outperform existing solutions through innovative post-training and reward modeling techniques.

-----

https://arxiv.org/abs/2412.15084

🤔 Original Problem:

→ Current LLMs struggle with complex mathematical reasoning, and existing math-specialized models lack robust evaluation mechanisms.

-----

🔧 Solution in this Paper:

→ AceMath employs a two-stage supervised fine-tuning process that first builds general competency across domains.

→ The model then undergoes targeted math-specific fine-tuning using carefully curated prompts and synthetic responses.

→ A specialized reward model, AceMath-RM, evaluates solutions across diverse problems and difficulty levels.

→ The system combines instruction-tuned models with reward models to achieve superior mathematical reasoning.

-----

💡 Key Insights:

→ Two-stage training significantly improves model performance compared to single-stage approaches

→ Larger models (72B) reduce the need for math-specific pre-training

→ High-quality synthetic data generation is crucial for effective training

→ Score-sorted sampling strategy enhances reward model training

-----

📊 Results:

→ AceMath-72B-Instruct outperforms Qwen2.5-Math-72B-Instruct by 3.68% average improvement

→ AceMath-7B matches GPT-4 performance while being significantly smaller

→ AceMath-72B-RM achieves highest rm@8 score across math reasoning benchmarks

------

Are you into AI and LLMs❓ Join my daily AI newsletter. I will send you 7 emails a week analyzing the highest signal AI developments. ↓↓

🎉 https://rohanpaul.substack.com/