M2.1: Multilingual and Multi-Task Coding with Strong Generalization

MiniMax-M2.1 represents a substantial advancement in coding capabilities, surpassing prior models and excelling in multilingual and multi-task scenarios, particularly in code generation, tool usage, and long-range planning. This open-source model, optimized for agentic scenarios, outperforms on benchmarks like SWE-Bench, which evaluates code generation and bug-fixing in real-world settings, although it highlights the necessity for broader language coverage and diverse task evaluations beyond bug-fixing. MiniMax-M2.1 addresses these gaps by building a multi-language training system across ten languages, enhancing its performance in complex environments and multi-task capabilities such as test generation and performance optimization. The model also demonstrates strong generalization across different scaffolds, maintaining high scores in various environments. Future directions include refining reward signals for developer experience, improving problem-solving efficiency, and exploring RL scaling, world model and user simulator development, and expanding scenario coverage to specialized fields, aiming to enhance the model's efficiency and applicability in real-world coding tasks.