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license: bsd-3-clause
pretty_name: T-Desk (OpenEnv)
tags:
- openenv
- reinforcement-learning
- agents
- terminal
configs:
- config_name: tasks
data_files:
- split: train
path: data/tasks.jsonl
- config_name: tasks_roomy
data_files:
- split: train
path: data/tasks_roomy.jsonl
T-Desk
These are the Workbench task families (real-library bug fixing, reflow and downtime audits, an expense audit, and route planning), served through an interface copied field for field from OpenEnv's tbench2_env. That is the interface the OpenEnv Arena operators' smoke tests use.
Interface
- Actions:
exec,write_file,evaluate. - Observations: the instruction is repeated every turn, and output is never trimmed.
- Reward: binary, through
evaluate. - Budget: 4,096 completion and 8,192 context tokens.
Pre-registered experiment. Does training under the eval's likely interface and budget move the private score, when other environments did not? The hypothesis, predictions and verdict rules are at https://github.com/moarshy/openenv-arena-notes/blob/main/versions/v6-tdesk.md
Calibration. Qwen3.8-27B, thinking off, strict JSON, arena budget accounting: 13/128 ≈ 10% solved. Failures were budget exhaustion (over-reading, re-verifying) and JSON format breaks.
- Image:
ghcr.io/moarshy/workbench:tdesk-v1 - Source: https://github.com/moarshy/workbench (
server/tdesk_environment.py) - Licences: the vendored libraries keep their own: toolz and boltons are BSD-3-Clause, tabulate and inflection are MIT.
v7: roomy variant (tdesk-roomy-v1)
The same tbench2 interface with Workbench v3's budget (12,288 / 16,384) and tasks (minus plant). It isolates the interface from the budget after v6 scored 0/40. Pre-registered at https://github.com/moarshy/openenv-arena-notes/blob/main/versions/v7-tdesk-roomy.md
- Image:
ghcr.io/moarshy/workbench:tdesk-roomy-v1 - Tasks:
data/tasks_roomy.jsonl - Calibration (strict JSON): 11/64. With room to work, format breaks, not the budget, dominate failures.