| # CrossCodeEval v2 |
|
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| ## 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 |
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|
| | 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 | |
|
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| ### Successful attempts among kept tasks |
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|
| | 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 |
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| 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 |
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|
| `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()) |
| ``` |
|
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| Trajectory statistics (plots, command distribution) are in [trace_stats](trace_stats/README.md). |
|
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| Not yet checked with the repository-leak checks added on 2026-10-17 (leftover git history and file timestamps that could reveal the solution). |
|
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| ## Runtime |
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| - **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). |
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| `patch.py` rebuilds `tasks.parquet` from the current v1 tasks and the recorded results, both pinned to revision `a43f5635`. |
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