| --- |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - agent |
| - code |
| - agentic-tasks |
| - harbor |
| - reinforcement-learning |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "*_v1/tasks.parquet" |
| - config_name: v2 |
| data_files: |
| - split: train |
| path: "*_v2/tasks.parquet" |
| - config_name: archive |
| data_files: |
| - split: train |
| path: "*_v1/archive.parquet" |
| --- |
| |
| # Validated tasks |
|
|
| Datasets repaired and filtered using the [OT-next-data validation protocol](https://github.com/MLI-lab/OT-next-data/blob/main/validation/PROTOCOL.md). |
|
|
| - **V1:** environment reliability and rule-based task-quality repairs. Build/setup, no-op and reference checks where reference solutions exist. |
| - **V2:** keep tasks solved at least once by an evaluated model. Includes partial results where a success is already recorded; each README lists models, attempt counts and coverage. |
|
|
| ## Dataset sizes and composition |
|
|
| **V1: 42,166 kept, patched tasks across 8 data sources.** |
|
|
| **V2: 7,684 tasks solved at least once across the same 8 data sources.** V2 is a subset of V1, not additional tasks. |
|
|
| Percentages are each source's share of that version's total in the current published snapshots. |
|
|
| | Dataset | V1 tasks | Share of V1 | V2 tasks | Share of V2 | |
| | --- | ---: | ---: | ---: | ---: | |
| | BugsInPy | [489](bugsinpy_v1) | 1.16% | [65](bugsinpy_v2) | 0.85% | |
| | CrossCodeEval | [6,710](crosscodeeval_v1) | 15.91% | [863](crosscodeeval_v2) | 11.23% | |
| | TermiGen | [3,336](termigen_v1) | 7.91% | [2,277](termigen_v2) | 29.63% | |
| | SETA | [3,107](seta_v1) | 7.37% | [2,205](seta_v2) | 28.70% | |
| | CalibForge | [5,317](calibforge_v1) | 12.61% | [84](calibforge_v2) | 1.09% | |
| | ScaleSWE | [16,786](scaleswe_v1) | 39.81% | [292](scaleswe_v2) | 3.80% | |
| | SWE-Lego | [4,277](swelego_v1) | 10.14% | [575](swelego_v2) | 7.48% | |
| | EndlessTerminals | [2,144](endless_v1) | 5.08% | [1,323](endless_v2) | 17.22% | |
| | **Total** | **42,166** | **100%** | **7,684** | **100%** | |
|
|
| `tasks.parquet` contains kept tasks, `archive.parquet` archived ones. ScaleSWE still uses its original Docker Hub images; we haven't consolidated them into shared images yet. |
|
|
| ## Download tasks and images |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| "FWeindel/validated-tasks", |
| repo_type="dataset", |
| allow_patterns=["seta_v2/tasks.parquet", "seta_v1/cached_images/**"], |
| local_dir="data", |
| ) |
| ``` |
|
|
| ## Trace stats |
|
|
| Trace stats put each attempt into one outcome category: |
|
|
| | Outcome | Meaning | |
| | --- | --- | |
| | Task complete | The agent said it was done and confirmed that it wanted to finish. The verifier still checks whether the solution is correct. | |
| | tmux session closed | The terminal closed before normal completion, for example because the agent sent an `exit` command or killed tmux. | |
| | Task timeout | The agent ran out of time. | |
| | Repeated output truncation | Three responses in a row were too long (current default: 8,192 output tokens) and could not be used. | |
| | Other limits | The agent hit another limit, such as the number of turns, response length, context size or task memory. | |
| | Environment error | The container could not start, or a terminal command or communication failed. Confirmed terminal closures go under “tmux session closed.” | |
| | Verifier error | The checker could not run or finish normally. A solution simply failing its tests is not a verifier error. | |
| | Model server error | A request to the model failed, for example because the server was unavailable or the request timed out. | |
| | Harness error | The software running the agent failed, for example because of a bug in retry handling. | |
| | Other error | An error was recorded, but it does not fit the categories above. | |
| | Not recorded | The saved result does not say how the attempt ended. | |
|
|
| These labels come from the harness's recorded stop reason and error type, not an LLM judge. |
|
|
