---
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.