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AI Research
arXiv CS.AI·May 20·5 min read
What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
TL;DR
A gap exists in understanding how code contributes to reasoning in language models (LMs). Controlled pretraining experiments were conducted using a 10 trillion token corpus to assess the impact of code.
✦ Why It Matters
Engineers can enhance language model training by incorporating structured code to improve reasoning capabilities.
Key Takeaways
- Incorporate standalone executable programs in LM training.
- Control for Code-NL data to isolate effects.
- Focus on structured reasoning signals for better performance.
- Explore fine-grained domain separation in datasets.
Full Summary
Language models (LMs) have increasingly incorporated code, yet its specific benefits for reasoning tasks remain ambiguous. To investigate this, researchers conducted controlled pretraining experiments on a 10 trillion token corpus, focusing on fine-grained domain separation.
They specifically analyzed the effects of standalone executable programs while controlling for Code-NL data. Findings revealed that the inclusion of code markedly improved reasoning capabilities in programming contexts.
This improvement suggests that structured reasoning signals from code can enhance LMs' performance. The study provides insights into optimizing LM training by strategically integrating code.
These results have implications for developing more effective AI systems that require reasoning skills.