Datasets:
File size: 2,519 Bytes
6a0f908 e96fd34 6a0f908 e96fd34 6a0f908 e96fd34 6a0f908 e96fd34 6a0f908 e96fd34 6a0f908 a4df902 e96fd34 a4df902 e96fd34 a4df902 e96fd34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | # CrossCodeEval v2
## Dataset stats
- **v1 tasks evaluated:** 6,710
- **Kept:** 863 (solved at least once by either model)
- **Excluded:** 5,847 (no recorded success)
- **Attempts:** pass@16 with Qwen3-30B Instruct and Qwen3-30B Thinking (214,720 in total)
## Pass@16 filtering
| Model | Tasks evaluated | Attempts | Tasks solved at least once |
| --- | ---: | ---: | ---: |
| Qwen3-30B Instruct | 6,710 | 107,360 | 641 |
| Qwen3-30B Thinking | 6,710 | 107,360 | 627 |
| Either model | 6,710 | 214,720 | 863 |
### Successful attempts among kept tasks
| Successful attempts | Instruct tasks | Thinking tasks |
| --- | ---: | ---: |
| 0/16 | 222 | 236 |
| 1/16 | 222 | 193 |
| 2/16 | 70 | 94 |
| 3/16 | 57 | 64 |
| 4/16 | 40 | 51 |
| 5/16 | 38 | 36 |
| 6/16 | 33 | 22 |
| 7/16 | 33 | 24 |
| 8/16 | 20 | 24 |
| 9/16 | 22 | 21 |
| 10/16 | 17 | 16 |
| 11/16 | 21 | 16 |
| 12/16 | 18 | 19 |
| 13/16 | 17 | 12 |
| 14/16 | 13 | 13 |
| 15/16 | 7 | 7 |
| 16/16 | 13 | 15 |
## Select tasks by successful-attempt count
You can select tasks by how often they were solved, for example all tasks Instruct solved exactly once:
```python
import pyarrow.parquet as pq
subset = pq.read_table("tasks.parquet", filters=[("instruct_successes", "=", 1)])
```
## Traces
`traces/` holds all 214,720 recorded attempts, successful and failed, from every evaluated task, one row per attempt (Zstandard-compressed Parquet). Columns include `task_id`, `model`, `attempt_id`, `reward`, `verifier_reward`, `retained_v2` and the full `trajectory` and `result` JSON. 1 attempts have no saved conversation (`trajectory_found=false`).
```python
import json
import pyarrow.dataset as ds
traces = ds.dataset("traces", format="parquet")
failed = traces.to_table(filter=ds.field("reward") == 0)
conversation = json.loads(failed["trajectory"][0].as_py())
```
Trajectory statistics (plots, command distribution) are in [trace_stats](trace_stats/README.md).
Not yet checked with the repository-leak checks added on 2026-10-17 (leftover git history and file timestamps that could reveal the solution).
## Runtime
- **Versions:** Apptainer **1.5.4**, Python **3.12.14**, Harbor **0.8.1**.
- **Implementation:** [Marin Harbor `7faf878c14b6`](https://github.com/marin-community/harbor/tree/7faf878c14b6d72737579ec936ebf5f74ba3194d) + [our runtime patches](https://github.com/MLI-lab/OT-next-data/tree/main/harbor_patches).
`patch.py` rebuilds `tasks.parquet` from the current v1 tasks and the recorded results, both pinned to revision `a43f5635`.
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