metadata
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.