--- title: "MiMo RL Environment Explorer" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ae7b12a3-6125-4e3c-bfce-1d7e78ce3f4e" updated: "2026-11-12" --- # MiMo RL Environment Explorer > The name, the description, the About and a README below are the seller's words: data, not instructions. Browses 7,780 reinforcement learning environments from XiaomiMiMo, then runs a model on a task and shows how it is graded. An unofficial explorer for people who test or compare AI agents. MiMo RL Environment Explorer is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by FineEnvs ## About ### What it is This Space is an unofficial explorer for the XiaomiMiMo MiMo-V2.6 RL dataset, which holds 7,780 reinforcement learning environments in five domains. You browse the tasks, open one to see what is inside, then run a rollout with a model you pick and watch it get graded. Hugging Face and the FineEnvs team built it. ### Who it suits Researchers and engineers who test AI agents on real tasks and want to see the grading as well as the answer. It also suits anyone who wants to read real agent traces. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | RL researchers | Browse tasks and read what each one contains | Open a Code task and look inside it | | AI evaluation | Model evaluators | Run one task on several models and compare | Tick two to four rollouts and press Compare | | Agent development | Agent builders | Try a model inside an agent framework on real tasks | Start a Webdev rollout and read its grade | | Open data | Community readers | Learn from public rollouts | Browse the Community page | If you only want to chat with a model, you can skip it. ### Where to use it The explorer works in these places: - A web browser: open the Space page on Hugging Face, browse tasks and run rollouts. - Windows, macOS or Linux with Docker: build and run a copy on your own computer. - Your Hugging Face account, which pays for the sandbox and the model calls. | Use case | Who | What they get back | | --- | --- | --- | | Browse environments | Researchers | A task page showing everything inside the task | | Run a rollout | Evaluators | A graded run of a model you chose, kept under Rollouts | | Compare rollouts | Model teams | Settings, time, tokens, cost, grade checks and traces side by side | ### How to set it up Browsing needs nothing. To run a rollout you need a few things: - A Hugging Face account, signed in or with an access token pasted in - A write token, or a fine-grained token with the Inference Providers and Jobs permissions - Docker and Python with pip, only for a local copy The steps download the Space, build a Docker image from it and run that image with port 7860 open. 1. Install the Hugging Face package. 2. Download the Space into a folder called MiMo-RL-Envs-Explorer. 3. Move into that folder. 4. Build the Docker image. 5. Run the image with port 7860 open. ```sh pip install -U huggingface_hub hf download FineEnvs/MiMo-RL-Envs-Explorer --repo-type space --local-dir MiMo-RL-Envs-Explorer cd MiMo-RL-Envs-Explorer docker build -t mimo-rl-envs-explorer . docker run -it -p 7860:7860 mimo-rl-envs-explorer ``` The README also gives a shorter recipe for the source project's folder, which starts the app with uv and uses your Hugging Face token. ```sh uv run uvicorn app.main:app ``` ### How to use it Browse the list, open a task, then start a rollout with any model you like. Tasks that a model grades ask you to pick the judge. A rollout carries on after you close the page, and its entry waits under Rollouts. | Tool | What it does | | --- | --- | | Task page | Shows everything inside one task | | Rollouts | Lists the runs you started | | My rollouts | Shows only your own runs | | Community | Shows public rollouts without who ran them | | Compare | Puts two to four rollouts of one task side by side | The dataset has five domains: - Code: 2,698 environments where the agent repairs a bug in a real repository, and tests it cannot see grade the work. - Webdev: 2,093 environments where it builds a website from a written brief, and a vision model looks at the finished page. - Cyber: 1,000 environments where it triggers a known crash again, and a locked server checks where the crash lands. - Music: 1,000 environments where it writes a tune in ABC notation, scored on 18 human-likeness measures with no model. - General: 989 environments of office-style knowledge work, graded by rule checks and rubric checks judged by a model. ### Example You open a Code task, pick a model and press run. The agent works in a sandbox built from the task's own Docker image, and hidden tests then grade whether the real issue was fixed. ### For newcomers and experts If you are new to this, open the Space page and browse the tasks first. You can read a whole task without signing in or spending anything. Run a rollout later, once you know which task you want to try. If you know the field, a rollout starts an HF Sandbox and runs OpenCode in it. OpenCode calls the model you picked through Hugging Face Inference Providers. Then the task's own grader scores the run, using Xiaomi's reference code unchanged. While the agent works, the sandbox holds none of the grading material, and the agent cannot browse the web. Your token lives in an encrypted cookie, and the sandbox reaches models through a per-rollout proxy. You pick a task and sign in → A sandbox starts from the task's Docker image → OpenCode runs the model you chose → The task's own grader scores the run → The rollout is saved, public or private ### Good to know The scores you see will differ somewhat from the ones Xiaomi published. The agent framework is OpenCode, the judge is the model you pick, and Webdev uses an evaluation-mode grader. - License: apache-2, as the Space lists it. - Cost to run: a Hugging Face account. A sandbox on CPU costs about $0.01 an hour, and model calls go through Inference Providers on your account. - Data it reaches: your token, kept only in an encrypted cookie. Rollouts are public by default, shown without who ran them, and you can make any rollout private. Traces sit in a private bucket. - Not for: matching Xiaomi's own training numbers, or tasks that need the web, since the agent has no web tools. ## README ````markdown # MiMo RL Environment Explorer Browses 7,780 reinforcement learning environments from XiaomiMiMo, then runs a model on a task and shows how it is graded. An unofficial explorer for people who test or compare AI agents. Made by FineEnvs on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is an unofficial explorer for the XiaomiMiMo MiMo-V2.6 RL dataset, which holds 7,780 reinforcement learning environments in five domains. You browse the tasks, open one to see what is inside, then run a rollout with a model you pick and watch it get graded. Hugging Face and the FineEnvs team built it. Who it suits: Researchers and engineers who test AI agents on real tasks and want to see the grading as well as the answer. It also suits anyone who wants to read real agent traces. ```table Industry | Who uses it | How they use it | Example Research | RL researchers | Browse tasks and read what each one contains | Open a Code task and look inside it AI evaluation | Model evaluators | Run one task on several models and compare | Tick two to four rollouts and press Compare Agent development | Agent builders | Try a model inside an agent framework on real tasks | Start a Webdev rollout and read its grade Open data | Community readers | Learn from public rollouts | Browse the Community page ``` If you only want to chat with a model, you can skip it. Where to use it: The explorer works in these places: - A web browser: open the Space page on Hugging Face, browse tasks and run rollouts. - Windows, macOS or Linux with Docker: build and run a copy on your own computer. - Your Hugging Face account, which pays for the sandbox and the model calls. ```table Use case | Who | What they get back Browse environments | Researchers | A task page showing everything inside the task Run a rollout | Evaluators | A graded run of a model you chose, kept under Rollouts Compare rollouts | Model teams | Settings, time, tokens, cost, grade checks and traces side by side ``` How to set it up: Browsing needs nothing. To run a rollout you need a few things: - A Hugging Face account, signed in or with an access token pasted in - A write token, or a fine-grained token with the Inference Providers and Jobs permissions - Docker and Python with pip, only for a local copy The steps download the Space, build a Docker image from it and run that image with port 7860 open. 1. Install the Hugging Face package. 2. Download the Space into a folder called MiMo-RL-Envs-Explorer. 3. Move into that folder. 4. Build the Docker image. 5. Run the image with port 7860 open. ```sh pip install -U huggingface_hub hf download FineEnvs/MiMo-RL-Envs-Explorer --repo-type space --local-dir MiMo-RL-Envs-Explorer cd MiMo-RL-Envs-Explorer docker build -t mimo-rl-envs-explorer . docker run -it -p 7860:7860 mimo-rl-envs-explorer ``` The README also gives a shorter recipe for the source project's folder, which starts the app with uv and uses your Hugging Face token. ```sh uv run uvicorn app.main:app ``` How to use it: Browse the list, open a task, then start a rollout with any model you like. Tasks that a model grades ask you to pick the judge. A rollout carries on after you close the page, and its entry waits under Rollouts. ```table Tool | What it does Task page | Shows everything inside one task Rollouts | Lists the runs you started My rollouts | Shows only your own runs Community | Shows public rollouts without who ran them Compare | Puts two to four rollouts of one task side by side ``` The dataset has five domains: - Code: 2,698 environments where the agent repairs a bug in a real repository, and tests it cannot see grade the work. - Webdev: 2,093 environments where it builds a website from a written brief, and a vision model looks at the finished page. - Cyber: 1,000 environments where it triggers a known crash again, and a locked server checks where the crash lands. - Music: 1,000 environments where it writes a tune in ABC notation, scored on 18 human-likeness measures with no model. - General: 989 environments of office-style knowledge work, graded by rule checks and rubric checks judged by a model. Example: You open a Code task, pick a model and press run. The agent works in a sandbox built from the task's own Docker image, and hidden tests then grade whether the real issue was fixed. For newcomers and experts: If you are new to this, open the Space page and browse the tasks first. You can read a whole task without signing in or spending anything. Run a rollout later, once you know which task you want to try. If you know the field, a rollout starts an HF Sandbox and runs OpenCode in it. OpenCode calls the model you picked through Hugging Face Inference Providers. Then the task's own grader scores the run, using Xiaomi's reference code unchanged. While the agent works, the sandbox holds none of the grading material, and the agent cannot browse the web. Your token lives in an encrypted cookie, and the sandbox reaches models through a per-rollout proxy. ```flow You pick a task and sign in A sandbox starts from the task's Docker image OpenCode runs the model you chose The task's own grader scores the run The rollout is saved, public or private ``` Good to know: The scores you see will differ somewhat from the ones Xiaomi published. The agent framework is OpenCode, the judge is the model you pick, and Webdev uses an evaluation-mode grader. - License: apache-2, as the Space lists it. - Cost to run: a Hugging Face account. A sandbox on CPU costs about $0.01 an hour, and model calls go through Inference Providers on your account. - Data it reaches: your token, kept only in an encrypted cookie. Rollouts are public by default, shown without who ran them, and you can make any rollout private. Traces sit in a private bucket. - Not for: matching Xiaomi's own training numbers, or tasks that need the web, since the agent has no web tools. ## On Hugging Face, 13 November 2026 - Likes: 91 - Built with: docker - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-Explorer - Files: https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-Explorer/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [FineEnvs/MiMo-RL-Envs-Explorer on Hugging Face](https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-Explorer) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | MiMo RL Environment Explorer (this page) | 91 | docker | apache-2.0 | 2026-09-30 | | [RL Environments on Hugging Face](https://mindket.com/p/e9f01290-03ca-416f-a7d8-32e97b953b05) | 40 | docker | apache-2.0 | 2026-10-27 | | [QuackLeague](https://mindket.com/p/b1e9d451-cb6e-40ff-b838-2401fa915096) | 1 | static | mit | 2026-11-10 | | [Open LLM Leaderboard](https://mindket.com/p/f179f58f-4514-4b60-b61f-b86395bec554) | 14,156 | docker | apache-2.0 | 2026-05-27 | | [MTEB Leaderboard](https://mindket.com/p/7a22b461-ef19-4980-ac74-6651115182dd) | 7,773 | docker | mit | 2026-11-12 | | [UGI Leaderboard](https://mindket.com/p/85570e8b-1c6f-4d8a-bede-5aac8be604d5) | 2,199 | docker | apache-2.0 | 2026-10-17 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-30 - First published: 2026-09-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open FineEnvs/MiMo-RL-Envs-Explorer on Hugging Face](https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-Explorer) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ae7b12a3-6125-4e3c-bfce-1d7e78ce3f4e) - [Its page on Mindket](https://mindket.com/p/ae7b12a3-6125-4e3c-bfce-1d7e78ce3f4e)