MathSmith-Hard-Problem-Synthesizer-Qwen3-8B

Overview

• <rationale>: structured reasoning describing concept integration and difficulty design.
• <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.
2. Supervised Fine-tuning (SFT): Train the model on collected concept–explanation pairs to establish foundational understanding.
3. Reinforcement Learning (RL): Optimize the model using GRPO with rewards based on:
   • Structural validity
   • Reasoning complexity
   • Answer consistency
4. Weakness-Focused Self-Improvement: Iteratively identify and address model weaknesses by generating targeted problem variants.

Dependence

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

Citation