Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
📰 ArXiv cs.AI
Learn to generate high-quality, solver-adaptive problems using reasoning-driven data synthesis, enhancing training for large reasoning models
Action Steps
- Apply reasoning-driven data synthesis to generate problems adaptive to the solver's ability
- Configure data pipelines to balance problem difficulty and value
- Test the effectiveness of generated problems in training large reasoning models
- Compare the performance of models trained with synthesized data to those trained with human-curated datasets
- Build a framework to integrate reasoning-driven data synthesis into existing training workflows
Who Needs to Know This
Researchers and developers working on large reasoning models can benefit from this approach to generate high-quality, adaptive problems for training, improving model performance and efficiency
Key Insight
💡 Reasoning-driven data synthesis can generate high-quality, solver-adaptive problems, overcoming limitations of existing approaches
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🤖 Generate high-quality, adaptive problems for large reasoning models using reasoning-driven data synthesis! 💡
Key Takeaways
Learn to generate high-quality, solver-adaptive problems using reasoning-driven data synthesis, enhancing training for large reasoning models
Full Article
Title: Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis
Abstract:
arXiv:2511.09907v5 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing approaches face several challenges: (i) indiscriminate generation that ignores the solver's ability and yields low-value problems, or reliance on complex data pipelines to balance problem difficulty; and (ii) a lack of reasoning in problem generation, leading to shallo
Abstract:
arXiv:2511.09907v5 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing approaches face several challenges: (i) indiscriminate generation that ignores the solver's ability and yields low-value problems, or reliance on complex data pipelines to balance problem difficulty; and (ii) a lack of reasoning in problem generation, leading to shallo
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