---
license: apache-2.0
language:
- en
- zh
task_categories:
- other
tags:
- rl-environment
- reinforcement-learning
- harbor
- agent
- mimo
- mimo-v2.6-rl
- terminal
- terminal
- terminal-bench
- cli
size_categories:
- n<1K
viewer: false
---

[![View tasks in Harbor Visualiser](https://img.shields.io/badge/%F0%9F%A4%97%20Harbor%20Visualiser-View%20tasks-FFD21F?style=for-the-badge)](https://huggingface.co/spaces/HuggingFaceH4/harbor-visualiser?dataset=FineEnvs/MiMo-V2.6-RL-harbor-terminal)
[![MiMo RL Environment Explorer](https://img.shields.io/badge/%F0%9F%A4%97%20Explorer-Run%20a%20rollout-FFD21F?style=for-the-badge)](https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-Explorer)

# 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](https://huggingface.co/datasets/XiaomiMiMo/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`](https://huggingface.co/datasets/XiaomiMiMo/MiMo-V2.6-RL-oss) at `639865fd3374` |
| Reference harness | XiaomiMiMo/verl `a2ad9f6` + XiaomiMiMo/mimoagent `467f0a1` |

Part of a set of six: [code](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-code), [cyber](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-cyber), [general](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-general), [terminal](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-terminal), [webdev](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-webdev), [music](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-music).

## Layout

```text
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.py
```

Example: [task.toml](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-terminal/blob/main/tasks/candidate-0036-software-data-engineering/task.toml) ·
[instruction](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-terminal/blob/main/tasks/candidate-0036-software-data-engineering/instruction.md) ·
[verifier](https://huggingface.co/datasets/FineEnvs/MiMo-V2.6-RL-harbor-terminal/blob/main/tasks/candidate-0036-software-data-engineering/tests/test.sh).

## Run

```bash
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
```

The 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:

```bash
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](https://github.com/adithya-s-k/FineEnvs/tree/mimo-explorer-rollouts/mimo-explorer/mimo_harbor). 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.

Deviations from the reference, and why, are listed in the adapter's 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](https://huggingface.co/spaces/FineEnvs/MiMo-RL-Envs-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.md` and `parity_experiment.json`.

## Credits

The environments, their prompts, tests, verifiers, images and graders are the work of the
[Xiaomi MiMo](https://huggingface.co/XiaomiMiMo) team, released as
[XiaomiMiMo/MiMo-V2.6-RL-oss](https://huggingface.co/datasets/XiaomiMiMo/MiMo-V2.6-RL-oss) together with their training code
[XiaomiMiMo/verl](https://github.com/XiaomiMiMo/verl) and agent harness
[XiaomiMiMo/mimoagent](https://github.com/XiaomiMiMo/mimoagent). mimoagent builds on
[mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent) (Kilian A. Lieret and Carlos E. Jimenez).
The task format and runner are [Harbor](https://github.com/harbor-framework/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.