MathSmith-HC-Problem-Synthesizer-Qwen3-8B

Overview

• <rationale>: structured reasoning describing concept integration and difficulty design strategies.
• <problem>: a single Olympiad-level mathematical question that admits a verifiable numeric or symbolic answer.

MathSmith Pipeline

1. Concept Collection: Randomly sample concept–explanation pairs from PlanetMath to ensure data independence and avoid benchmark contamination.
2. Supervised Fine-tuning (SFT): Train the model on collected concept–explanation pairs to establish foundational understanding of problem generation.
3. Reinforcement Learning (RL): Optimize the model using GRPO with rewards based on structural validity, reasoning complexity (trace length), and answer consistency.
4. Weakness-Focused Self-Improvement: Iteratively identify and address model weaknesses by generating targeted problem variants for specific mathematical concepts.

Dependence

• Transformers 4.52.4
• Pytorch 2.7.0+cu126
• Datasets 3.6.0
• Tokenizers 0.21.1

Citation