| --- |
| license: other |
| license_name: mixed-permissive |
| license_link: LICENSE.md |
| tags: |
| - math |
| - reinforcement-learning |
| - rl-environment |
| - harbor |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # MathConstructOptimize-Envs (Harbor) |
|
|
| The 3,577 tasks of [`amphora/MathConstructOptimize-Envs`](https://huggingface.co/datasets/amphora/MathConstructOptimize-Envs) as runnable [Harbor](https://github.com/laude-institute/harbor) environments. Each task is one directory: |
|
|
| ``` |
| tasks/<task_id>/ |
| task.toml # metadata (subset, family, problem_key, tier, level, source, license, tags, |
| # direction/baseline/best_known for optimize), limits, network_mode = "no-network" |
| instruction.md # the prompt; the agent writes its answer to /workdir/answer.json |
| environment/Dockerfile # python:3.11 slim + pinned numpy/scipy/sympy/networkx/pysat/z3/pulp/ortools |
| environment/instance.json |
| tests/test.sh # runs tests/run.py + tests/check.py, writes /logs/verifier/reward.txt |
| solution/solve.sh # oracle: a known valid answer |
| ``` |
|
|
| ```bash |
| uv tool install harbor |
| huggingface-cli download amphora/MathConstructOptimize-Envs-harbor --repo-type dataset --local-dir mcoe |
| harbor run -p mcoe/tasks -a oracle -n 16 # validate: every task scores > 0 |
| harbor run -p mcoe/tasks -a terminus-2 -m <model> -n 16 |
| ``` |
|
|
| **Validation:** |
| - The oracle agent passes on all 3,577 tasks (construct = 1.0, optimize ≥ 0.1), and a no-op agent scores 0. |
| - An HTTP canary confirmed that `no-network` blocks internet access inside the task container. Research-level best constructions are public, so do not relax this for RL. |
| - If you run many trials concurrently, raise Docker's default address pools. Each no-network task creates its own networks, and the defaults are exhausted at roughly 30 concurrent trials. |
|
|
| See the main dataset card for the reward contract, tags, sources and the upstream verifier bugs we fixed. |
|
|
