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language:
- en
license: mit
task_categories:
- reinforcement-learning
configs:
- config_name: default
data_files:
- split: train
path: tasks.jsonl
---
Original QE tasks cover subtitles, timecodes, catalogues, assets and exact rational ledger aggregation. Reward is one when all original output-artifact checks pass, otherwise zero.
Build `components/qefiles/Dockerfile` and run the image on port 8000. Reset with `task_id` set to `qe_skill-media-d2` or `qe_skill-math-d4`; an explicit seed reproduces an instance. Actions are `exec`, `write_file` and `evaluate`. Use one active session per sandbox.
Code and synthetic task data are by Dragan Spiridonov, licensed under MIT. Source and SDK revisions are recorded in `components/qefiles/SOURCE_MANIFEST.json`; original notices are retained. Run `python3 -B validate_dataset.py` to verify the inventory.
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