---
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:** retain 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: 7,629 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) | 11.31% |
| TermiGen | [3,336](termigen_v1) | 7.91% | [2,277](termigen_v2) | 29.85% |
| SETA | [3,107](seta_v1) | 7.37% | [2,150](seta_v2) | 28.18% |
| CalibForge | [5,317](calibforge_v1) | 12.61% | [84](calibforge_v2) | 1.10% |
| ScaleSWE | [16,786](scaleswe_v1) | 39.81% | [292](scaleswe_v2) | 3.83% |
| SWE-Lego | [4,277](swelego_v1) | 10.14% | [575](swelego_v2) | 7.54% |
| EndlessTerminals | [2,144](endless_v1) | 5.08% | [1,323](endless_v2) | 17.34% |
| **Total** | **42,166** | **100%** | **7,629** | **100%** |

Percentages may not sum to exactly 100% due to rounding.

`tasks.parquet` contains kept tasks. V1 includes `archive.parquet`; V2 exclusions can be recovered from V1 using the selection manifest. V2 reuses V1 images. ScaleSWE uses its original Docker Hub images, with optional Apptainer caches.

## 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",
)
```

Image metadata and reproduction scripts pin immutable source revisions. Their historical paths remain valid at those revisions despite the folder renames on main.

CalibForge's pilot LLM-review results are in [the GitHub review JSON](https://github.com/MLI-lab/OT-next-data/blob/main/data/calibforge/stage2-review-171/reviews.json). LLM-judge filtering has not been applied across the full release.