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R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning
arXiv – CS AI|Yongchao Chen, Yueying Liu, Junwei Zhou, Yilun Hao, Jingquan Wang, Yang Zhang, Na Li, Chuchu Fan|
🤖AI Summary
Researchers developed R1-Code-Interpreter, a large language model that uses multi-stage reinforcement learning to autonomously generate code for step-by-step reasoning across diverse tasks. The 14B parameter model achieves 72.4% accuracy on test tasks, outperforming GPT-4o variants and demonstrating emergent self-checking capabilities through code generation.
Key Takeaways
- →R1-Code-Interpreter extends text-only LLMs using supervised fine-tuning and reinforcement learning to generate code queries for reasoning tasks
- →Multi-stage curriculum learning approach improved RL training gains from 3.4% to 9.3% across Qwen-2.5 models
- →The final R1-CI-14B model achieved 72.4% accuracy, surpassing GPT-4o (58.6%) and GPT-4o with Code Interpreter (70.9%)
- →Training was conducted across 144 diverse reasoning and planning tasks rather than narrow domain-specific applications
- →The model exhibits emergent self-checking behavior through autonomous code generation capabilities
#large-language-models#code-interpreter#reinforcement-learning#ai-reasoning#supervised-learning#qwen#gpt-4o#curriculum-learning#code-generation
Read Original →via arXiv – CS AI
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