InternBootcamp is an open-source framework providing 1000+ diverse, verifiable task environments for LLM reasoning research. It simplifies integrating varied reasoning tasks for model optimization, synthetic data generation, and evaluation. Its core benefit is enhanced LLM reasoning performance and training efficiency via "Task Scaling," exposing models to a wide task spectrum.

How It Works

The framework standardizes task integration with a unified interface for RL or synthetic data pipelines. Its key innovation, "Task Scaling," uses an automated agent workflow to synthesize 1000+ diverse, verifiable reasoning tasks across 8 domains. This workflow includes task collection, evolutionary code generation, and unittest filtering, enabling scalable expansion. The approach facilitates broad experiential learning and emergent abilities, making initially unsolvable tasks learnable through cross-task exposure.

Quick Start & Requirements

• Installation: Clone the repository (git clone https://github.com/InternLM/InternBootcamp.git), navigate (cd InternBootcamp), and install (pip install -e .).
• Prerequisites: Python environment, Git. No specific hardware or Python version requirements are explicitly stated.
• Resources: Links to the project's paper, GitHub repository, and evaluation benchmarks are available.

Highlighted Details

• Features 1000+ complex reasoning tasks across 8 domains, including algorithms, puzzles, scientific reasoning, and benchmarks like ARC-AGI and BBEH.
• Over 90% of tasks are automatically synthesized via an evolutionary pipeline, enabling rapid, scalable expansion.
• Demonstrates "Task Scaling" improves LLM performance and training efficiency, fostering emergent abilities where tasks unsolvable in isolation become learnable.
• InternThinker-GO, trained using the framework, achieves performance comparable to professional Go players, surpassing current LLM reasoning models.

Maintenance & Community

• Recent updates include v1.0 and a technical report, both dated August 2025, indicating recent development.
• The project encourages community contributions for expanding task scope and verifying generated bootcamps.
• Acknowledgments mention integrations with projects like Intern-S1, VeRL, Xtuner, and OpenCompass.

Licensing & Compatibility

• The provided README does not specify the project's license. This omission requires clarification for adoption decisions, particularly regarding commercial use or derivative works.

Limitations & Caveats

• The framework excludes tasks requiring specific world-view knowledge, such as counterfactual reasoning.
• The automated generation pipeline filters out bootcamps with accuracy outside a specific range ([0.03, 0.85]), potentially excluding edge cases.
• Future-dated release information suggests the project may still be under active, potentially unstable, development.