Publish SETA v2 using verified partial teacher results

seta_v2/README.md

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+# SETA v2
+
+## Dataset stats
+
+- **Tasks:** 4,567 native tasks; 1,414 omitted upstream, 46 excluded in v1. **3,107 v1 tasks → 2,150 retained**, all previously patched; **957 temporarily excluded** in v2.
+- **Images:** reuse **10 of v1's 11 cached Apptainer environments**. No additional images or task repairs in v2; original unique image count was not recorded.
+
+## Dataset characteristics
+
+<details><summary>Domains</summary>
+
+**Data Science and Machine Learning:** data cleaning, analysis, scikit-learn models and reports. **Debugging:** Python/pandas repairs. **System Administration:** shell and system configuration. **Data Processing:** file inventories, policy checks and structured outputs.
+
+</details>
+<details><summary>Capabilities</summary>
+
+**Specification Adherence:** exact paths, schemas, metrics and constraints. **Exploration:** inspect data, policies and broken scripts. **Error Recovery:** reproduce failures and verify repairs. Labels follow [CLI-Universe](https://arxiv.org/abs/2606.22883v1).
+
+</details>
+
+Intended benchmark transfer: **Terminal-Bench 2.0, OpenThoughts-TBLite 2.0, DS-1000 mini-200 and BixBench CLI corrections @ Pi**, through terminal workflows, Python data analysis and executable verification.
+
+## Partial-result selection
+
+Snapshot: **15 October 2026, 20:57 UTC**. Keep a task as soon as either model records a successful attempt, even if other attempts are unfinished. **811 retained tasks have incomplete results; 1,339 have all 16 graded attempts per model.**
+
+| Model | Tasks solved at least once |
+| --- | ---: |
+| Qwen3-30B-A3B-Instruct-2507 | 1,991 |
+| Qwen3-30B-A3B-Thinking | 1,356 |
+| Either model | **2,150** |
+
+| Temporary exclusion reason | Tasks |
+| --- | ---: |
+| Complete 16 attempts per model, no success | 364 |
+| Incomplete results, no success recorded yet | 564 |
+| No scored results yet | 29 |
+| Total | **957** |
+
+Missing results remain unknown, not failures. The 593 exclusions with missing/incomplete results can be reconsidered as runs finish. A lack of success does not establish a task defect.
+
+Build, reference, no-op and rule-based checks carry over from [v1](../camel-ai__SETA-Env_environment-reliable/README.md). Evaluated hashes match v1; retained payloads are unchanged.
+
+## Successful attempts among retained tasks
+
+These are observed success counts from up to 16 attempts per model, not complete pass@16 estimates. Zero for one model can include missing results; the other model solved the task.
+
+| Successful attempts | Instruct tasks | Thinking tasks |
+| --- | ---: | ---: |
+| 0 | 159 | 794 |
+| 1 | 240 | 271 |
+| 2 | 187 | 145 |
+| 3 | 117 | 91 |
+| 4 | 112 | 91 |
+| 5 | 119 | 74 |
+| 6 | 95 | 59 |
+| 7 | 94 | 71 |
+| 8 | 86 | 46 |
+| 9 | 70 | 57 |
+| 10 | 93 | 61 |
+| 11 | 103 | 63 |
+| 12 | 79 | 56 |
+| 13 | 118 | 63 |
+| 14 | 102 | 61 |
+| 15 | 136 | 73 |
+| 16 | 240 | 74 |
+
+## Files and selecting tasks
+
+`tasks.parquet` contains retained payloads and each model's successes, graded attempts and missing attempts. `assets/selection.json` records every v1 task's results, selection reason and the pinned v1 revision. Excluded payloads remain in v1; no duplicate archive Parquet is uploaded. `patch.py` reproduces this snapshot.
+
+```python
+import pyarrow.parquet as pq
+# Observed exactly one Instruct success, including incomplete runs.
+subset = pq.read_table("tasks.parquet", filters=[("instruct_successes", "=", 1)])
+# Add ("instruct_graded_attempts", "=", 16) to require a complete Instruct run.
+pq.write_table(subset, "selected.parquet", compression="zstd")
+```
+
+Traces are not included in this snapshot.
+
+## Runtime
+
+Apptainer **1.5.4**, Python **3.12.14**, Harbor **0.8.1**; [Marin Harbor bridge](https://github.com/marin-community/harbor/tree/7faf878c14b6d72737579ec936ebf5f74ba3194d) and [runtime patches](https://github.com/MLI-lab/OT-next-data/tree/main/harbor_patches).

seta_v2/patch.py

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+"""Reproduce the frozen SETA v2 partial-result selection. Requires pyarrow and huggingface_hub."""
+import argparse,copy,hashlib,json
+from pathlib import Path
+import pyarrow as pa
+import pyarrow.parquet as pq
+from huggingface_hub import hf_hub_download
+p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args()
+m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());a.out.mkdir(parents=True,exist_ok=True)
+assert not (a.out/'tasks.parquet').exists(),'Choose an empty output directory'
+f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'))
+assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
+t=pq.read_table(f);rows=[];schema=t.schema.remove_metadata()
+for prefix in ['instruct','thinking']:
+ for suffix in ['successes','graded_attempts','missing_attempts']:
+  schema=schema.append(pa.field(prefix+'_'+suffix,pa.int64()))
+schema=schema.append(pa.field('target_attempts_per_model',pa.int64()))
+for r in t.to_pylist():
+ s=m['tasks'][r['path']];assert r['content_sha256']==s['content_sha256']
+ if not s['kept']:continue
+ for prefix,model in [('instruct','qwen3-30b-instruct-2507'),('thinking','qwen3-30b-thinking')]:
+  v=s['models'][model];assert v['successful_attempts']==sum(x==1 for x in v['rewards'])
+  assert v['graded_attempts']==sum(x is not None for x in v['rewards'])
+  assert v['missing_attempts']==16-v['graded_attempts']
+  for suffix,key in [('successes','successful_attempts'),('graded_attempts','graded_attempts'),('missing_attempts','missing_attempts')]:r[prefix+'_'+suffix]=v[key]
+ assert r['instruct_successes'] or r['thinking_successes']
+ r['target_attempts_per_model']=16;rows.append(r)
+assert len(rows)==2150
+names={r['path'] for r in rows};images=json.loads(t.schema.metadata[b'ot.images.v1']);images['tasks']={k:v for k,v in images['tasks'].items() if k in names};assert set(images['tasks'])==names
+bundles={e['bundle'] for v in images['tasks'].values() for e in v['environments']};images['bundles']={k:v for k,v in images['bundles'].items() if k in bundles};images['missing']=[]
+meta={b'ot.images.v1':json.dumps(images).encode(),b'ot.release.v2':json.dumps({k:v for k,v in m.items() if k!='tasks'}).encode()}
+pq.write_table(pa.Table.from_pylist(rows,schema=schema).replace_schema_metadata(meta),a.out/'tasks.parquet',compression='zstd')
+print(json.dumps({'retained':len(rows),'images_reused':len(bundles)}))

seta_v2/tasks.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d447ca273c69e00b92abb677e6ae710d8e13bf345808b96f44c849ace5aa505
+size 224982161