This is huge. Xiaomi just open-sourced 7k+ reinforcement learning task environments used to train MiMo, covering code, cybersecurity, genera…

This is huge. Xiaomi just open-sourced 7k+ reinforcement learning task environments used to train MiMo, covering code, cybersecurity, general tool use, visual web development, and music-related tasks. here's why this matters more than another model release: open weights let you run a model. open environments let you teach one. reinforcement learning environments are difficult to build, because each task needs an executable world, a clear objective, tools the agent can use, and a reliable test for success. Xiaomi has released much of that missing infrastructure, including prompts, agent configurations, reward metadata, and the surrounding training framework. here is what developers can now do with it. → train models on real agent work. instead of imitating static answers, a model can attempt tasks, call tools, observe results, and improve from the outcome. → specialize smaller models. teams can train an open checkpoint for software engineering, vulnerability reproduction, knowledge work, or web development instead of relying on a much larger general-purpose model. → study complete trajectories. each run captures the agent’s reasoning, tool calls, environment feedback, and reward. researchers can identify where agents fail and compare training algorithms, reward strategies, harnesses, and models. → build a complete training loop. Xiaomi’s stack covers environment interaction, trajectory collection, reward evaluation, and policy optimization. developers can replace individual components and measure what improves behavior. the key shift is from open-sourcing model outputs to open-sourcing learning experiences. a dataset teaches a model what an answer looks like. an environment lets it discover which actions lead to success. check this out on HuggingFace 🤗: http://huggingface.co/datasets/XiaomiMiMo/MiMo-V2.6-RL-oss i wrote a complete breakdown of fine-tuning models with reinforcement learning using GRPO and RULER. RULER uses a language model judge to rank trajectories, so you do not have to manually write reward functions or collect labeled answers. the article is quoted below.

Source: Clem Delangue (X) | 2026-09-26

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