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Validated tasks
Datasets repaired and filtered using the OT-next-data validation protocol.
- V1: environment reliability and rule-based task-quality repairs. Build/setup, no-op and reference checks where reference solutions exist.
- V3: newly solved tasks absent from v2, with the new evaluation's traces and statistics. Combine v2 and v3 for the full solved set.
- 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.
Incremental v3 releases
V3 task files contain only newly solved tasks, with no task-ID overlap with the corresponding v2 task file. Historical traces remain in v2; v3 includes every attempt from its new evaluation, successful and failed. These subsets were harder for the earlier evaluated models, not necessarily for every future model.
| Dataset | v2 tasks, available separately | Newly solved v3 tasks | Combined unique tasks |
|---|---|---|---|
| BugsInPy | 65 | 406 | 471 |
| CalibForge | 1,512 | 2,287 | 3,799 |
| EndlessTerminals | 1,958 | 121 | 2,079 |
| NL2Bash | 938 | 104 | 1,042 |
| TermiGen | 2,277 | 230 | 2,507 |
Each release README includes a v2 + v3 loading example. Counts here describe the incremental releases published on 2026-10-26. Earlier cumulative v3 versions remain accessible by their pinned HF commit revisions.
Earlier v1/v2 size snapshot
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.
The table below is an earlier snapshot and predates subsequent evaluations. For current counts, use the individual release READMEs and the v3 table above. Percentages refer to this earlier snapshot.
| Dataset | V1 tasks | Share of V1 | V2 tasks | Share of V2 |
|---|---|---|---|---|
| BugsInPy | 489 | 1.16% | 65 | 0.85% |
| CrossCodeEval | 6,710 | 15.91% | 863 | 11.23% |
| TermiGen | 3,336 | 7.91% | 2,277 | 29.63% |
| SETA | 3,107 | 7.37% | 2,205 | 28.70% |
| CalibForge | 5,317 | 12.61% | 84 | 1.09% |
| ScaleSWE | 16,786 | 39.81% | 292 | 3.80% |
| SWE-Lego | 4,277 | 10.14% | 575 | 7.48% |
| EndlessTerminals | 2,144 | 5.08% | 1,323 | 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
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.
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