R1-Code-Interpreter: LLMs Reason with Code via Supervised and Multi-stage Reinforcement Learning

Practical guidance on training Large Language Models (LLMs) to leverage Code Interpreter across diverse tasks remains lacking. Our final model, R1-CI-14B, improves average accuracy on the 37 test tasks from 44.1% to 72.4%, Notably, R1-CI-14B also exhibits emergent self-checking behavior through code generation. While reinforcement learning (RL)-based fine-tuning has significantly improved LLMs' reasoning and planning Wang In contrast, symbolic code generation handles these rigorously and benefits from external tools (e.g., A key challenge is guiding LLMs to decide when to rely on textual reasoning versus programmatic solutions, given that most input questions lack explicit cues about which approach is best and the possible text/code solution space is large. OpenAI's GPT models address this by incorporating a Code Interpreter, allowing iterative code generation Interpreter implementations struggle to effectively steer between text and code, underutilizing symbolic capabilities. Recent work such as ToRL (Li et al., 2025b) and ReTool (Feng et al., 2025) investigates training reasoning models to integrate with Code Interpreters. To tackle these challenges, we present R1-Code-Interpreter, a framework for integrating Code Interpreter into open-source LLMs. We curate 144 reasoning and planning tasks and synthesize 6.5k multi-turn text/code trajectories for This difficulty arises from task heterogeneity and the scarcity of effective samples.