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MiMo-V2.6-RL Terminal (Harbor)
Terminal-Bench style tasks. 64 Harbor tasks from the Terminal domain of Xiaomi's MiMo-V2.6-RL-oss, the RL environments MiMo-V2.6 was trained on, converted so every one runs as a standard Harbor task.
Self-contained command-line tasks in the Terminal-Bench format, graded by each task's own pytest suite and anti-hack guard.
| Tasks | 64 |
| Graded by | pytest + anti-hack guard (deterministic) |
| Reference step limit | 500 |
| Source | XiaomiMiMo/MiMo-V2.6-RL-oss at 639865fd3374 |
| Reference harness | XiaomiMiMo/verl a2ad9f6 + XiaomiMiMo/mimoagent 467f0a1 |
Part of a set of six: code, cyber, general, terminal, webdev, music.
Layout
tasks/<task_id>/
├── task.toml # image pinned by digest, one-time setup (healthcheck), judge settings, provenance
├── instruction.md # the prompt the agent sees, word for word what Xiaomi's harness gives it
├── environment/ # Dockerfile (FROM the same digest) and setup/, a readable copy of the setup
└── tests/ # test.sh and everything grading needs; uploaded only after the agent finishes
registry.json # Harbor registry entry
data/tasks.jsonl # one metadata row per task, for filtering without walking the tree
manifest.json # sha256 of every task directory
jobs/terminal.yaml # the reference agent settings
agents/mimo_opencode.pyExample: task.toml · instruction · verifier.
Run
hf download FineEnvs/MiMo-V2.6-RL-harbor-terminal --repo-type dataset --local-dir mimo-terminal
cd mimo-terminal
PYTHONPATH=agents HF_TOKEN=hf_... harbor run -y -c jobs/terminal.yaml
# or on Daytona (builds the Dockerfile, runs the agent unprivileged natively — closer to Xiaomi's pods):
DAYTONA_API_KEY=... DAYTONA_API_URL=... PYTHONPATH=agents HF_TOKEN=hf_... harbor run -y -c jobs/terminal.daytona.yamlThe job runs agents/mimo_opencode.py (OpenCode 1.18.32) on HF Sandbox with the reference settings: the step
limit above, replies of up to 65,536 tokens, thinking low, no web fetch or search.
Change model_name and the provider block for another model. Any Harbor agent can run these tasks; this one
also applies two steps of Xiaomi's harness a task file can't express: the answer-leak blocklist after install,
and the unprivileged agent user on sandboxes that run everything as root.
Serving them to a trainer works like any Harbor dataset, for example with OpenEnv:
openenv harbor serve --dataset FineEnvs/MiMo-V2.6-RL-harbor-terminal --llm-url http://127.0.0.1:8000/v1 --model <your-model>How it was converted
With the mimo_harbor adapter. It reads the source at one pinned revision and every image through
a digest lock, so a rerun produces byte-identical tasks (manifest.json). Setup and grading follow Xiaomi's
harness step by step, as the explorer's runner does, using the same vendored graders:
- Nothing that grades a task (hidden tests, rubrics, verifier scripts) is reachable while the agent works.
- A testbed failure (a patch that won't apply, a system that died, a judge that never answered) writes no reward, so Harbor records an error rather than a 0.
- Images are pinned by digest; HF Sandbox and other prebuilt-image backends run them without a build step.
Where this differs from Xiaomi's setup, in plain terms
The task, its files, how it is graded, and the reward you get are exactly Xiaomi's. These are the only differences, and how we handle each:
- The agent is OpenCode, not Xiaomi's own agent. The model gets the same instruction and the same reward — only the tool it uses to do the work is different.
- It can't look the answer up online. Xiaomi ran with no internet. We let the model reach its own model API but block, by name, the places an answer could come from — code sites (GitHub, GitLab, …), search engines, this dataset's own page on the Hub, and package registries — so they don't resolve while it works.
That's the whole list. The detailed, technical version is in the adapter README.
Validation
-
All 7,780 tasks across the six datasets load with Harbor's task loader and pass the adapter's static checks (digest-pinned images, setup payload equal to its readable copy, rubrics only under
tests/). -
With Harbor's no-op agent every dataset scores 0 and every verifier runs to completion: no free rewards.
-
Parity with Xiaomi's harness. Following Harbor's parity procedure, the 36-task parity subset (6 per dataset) was run 3 times on each side with the same agent (OpenCode 1.18.32), model (GLM-5.3 via deepinfra, thinking low), step limits, judges and sandboxes, in Harbor and in the explorer, which runs Xiaomi's harness. The score ranges overlap for all six datasets. For this one:
Mean reward (mean ± SEM, 3 runs) Runs Xiaomi's harness (explorer) 0.278 ± 0.056 0.333, 0.333, 0.167 Harbor 0.111 ± 0.056 0.167, 0.167, 0.000 Each task is all or nothing, and outcomes flip in both directions on both sides; with six tasks the gap is within noise.
Full results, per-task outcomes and notes are in
parity.mdandparity_experiment.json.
Credits
The environments, their prompts, tests, verifiers, images and graders are the work of the Xiaomi MiMo team, released as XiaomiMiMo/MiMo-V2.6-RL-oss together with their training code XiaomiMiMo/verl and agent harness XiaomiMiMo/mimoagent. mimoagent builds on mini-swe-agent (Kilian A. Lieret and Carlos E. Jimenez). The task format and runner are Harbor. This conversion is by Hugging Face and the FineEnvs team, and is not affiliated with or endorsed by Xiaomi.
License
Apache-2.0, as the source dataset (LICENSE). Files vendored from XiaomiMiMo/mimoagent (tests/server_arvo.py
in Cyber tasks, and the ported setup commands) are MIT (LICENSE-mimoagent.md). NOTICE lists every source,
its license, and the changes this conversion made. Vendored grader files are unmodified.