crosscodeeval_v2/README.md

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:

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).

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.

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 + our runtime patches.

patch.py rebuilds tasks.parquet from the current v1 tasks and the recorded results, both pinned to revision a43f5635.