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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
idx: int64
env: string
gt: string
recs: list<item: struct<reward: double, accuracy: int64, format_score: int64, len: int64, finish: string, (... 20 chars omitted)
child 0, item: struct<reward: double, accuracy: int64, format_score: int64, len: int64, finish: string, closed_thin (... 8 chars omitted)
child 0, reward: double
child 1, accuracy: int64
child 2, format_score: int64
child 3, len: int64
child 4, finish: string
child 5, closed_think: bool
text: list<item: string>
child 0, item: string
ids: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
to
{'idx': Value('int64'), 'env': Value('string'), 'gt': Value('string'), 'recs': List({'reward': Value('float64'), 'accuracy': Value('int64'), 'format_score': Value('int64'), 'len': Value('int64'), 'finish': Value('string'), 'closed_think': Value('bool')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
idx: int64
env: string
gt: string
recs: list<item: struct<reward: double, accuracy: int64, format_score: int64, len: int64, finish: string, (... 20 chars omitted)
child 0, item: struct<reward: double, accuracy: int64, format_score: int64, len: int64, finish: string, closed_thin (... 8 chars omitted)
child 0, reward: double
child 1, accuracy: int64
child 2, format_score: int64
child 3, len: int64
child 4, finish: string
child 5, closed_think: bool
text: list<item: string>
child 0, item: string
ids: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
to
{'idx': Value('int64'), 'env': Value('string'), 'gt': Value('string'), 'recs': List({'reward': Value('float64'), 'accuracy': Value('int64'), 'format_score': Value('int64'), 'len': Value('int64'), 'finish': Value('string'), 'closed_think': Value('bool')})}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
idx int64 | env string | gt string | recs list |
|---|---|---|---|
0 | BannedPointSupersetPathCounting | 1 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0,
"accuracy": 0,
"format_score": 1,
"len": 13214,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"fo... |
1 | BannedPointSupersetPathCounting | 6 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
2 | BannedPointSupersetPathCounting | 158 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
3 | BannedPointSupersetPathCounting | 984 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
4 | BannedPointSupersetPathCounting | 48 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
5 | BannedPointSupersetPathCounting | 3 | [
{
"reward": 0,
"accuracy": 0,
"format_score": 1,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"... |
6 | BannedPointSupersetPathCounting | 3 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0,
"accuracy": 0,
"format_score": 1,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"... |
7 | BannedPointSupersetPathCounting | 0 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
8 | BannedPointSupersetPathCounting | 13 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
9 | BannedPointSupersetPathCounting | 9 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
... |
10 | CoinSquareGame | 60 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
11 | CoinSquareGame | 90 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
12 | CoinSquareGame | 56 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
13 | CoinSquareGame | 65 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
14 | CoinSquareGame | 59 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0,
"accuracy": 0,
"format_score": 1,
"len": 15983,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"fo... |
15 | CoinSquareGame | 54 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
16 | CoinSquareGame | 64 | [
{
"reward": 0,
"accuracy": 0,
"format_score": 1,
"len": 16018,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"fo... |
17 | CoinSquareGame | 67 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
18 | CoinSquareGame | 49 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
19 | CoinSquareGame | 49 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
20 | CycleCounting | 2 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": 0.017341529915832606,
"accuracy": 0,
"format_score": 1,
"len": 12834,
"finish": "stop",
"closed_think": true
},
{
"reward": 0.0000169350... |
21 | CycleCounting | 2 | [
{
"reward": 0.000003625096370832828,
"accuracy": 0,
"format_score": 1,
"len": 16253,
"finish": "stop",
"closed_think": true
},
{
"reward": 0.00010485760000000006,
"accuracy": 0,
"format_score": 1,
"len": 14379,
"finish": "stop",
"closed_think": true
},
{
"... |
22 | CycleCounting | 1 | [
{
"reward": 9.5367431640625e-7,
"accuracy": 0,
"format_score": 1,
"len": 11162,
"finish": "stop",
"closed_think": true
},
{
"reward": 0.00001693508780843028,
"accuracy": 0,
"format_score": 1,
"len": 13056,
"finish": "stop",
"closed_think": true
},
{
"rewar... |
23 | CycleCounting | 3 | [
{
"reward": 2.929026719120961e-8,
"accuracy": 0,
"format_score": 1,
"len": 13588,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"ac... |
24 | CycleCounting | 1 | [
{
"reward": 1.0240000000000006e-7,
"accuracy": 0,
"format_score": 1,
"len": 11734,
"finish": "stop",
"closed_think": true
},
{
"reward": 1,
"accuracy": 1,
"format_score": 1,
"len": 9913,
"finish": "stop",
"closed_think": true
},
{
"reward": 1.0240000000000... |
25 | CycleCounting | 1 | [
{
"reward": 0.0009765625,
"accuracy": 0,
"format_score": 1,
"len": 12753,
"finish": "stop",
"closed_think": true
},
{
"reward": 1.6310376661280197e-13,
"accuracy": 0,
"format_score": 1,
"len": 12990,
"finish": "stop",
"closed_think": true
},
{
"reward": 1.... |
26 | CycleCounting | 6 | [
{
"reward": 0.0023312328394264987,
"accuracy": 0,
"format_score": 1,
"len": 12614,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0.00097656... |
27 | CycleCounting | 3 | [
{
"reward": 0.006046617599999997,
"accuracy": 0,
"format_score": 1,
"len": 9970,
"finish": "stop",
"closed_think": true
},
{
"reward": 0.0000059048999999999975,
"accuracy": 0,
"format_score": 1,
"len": 13687,
"finish": "stop",
"closed_think": true
},
{
"re... |
28 | CycleCounting | 1 | [
{
"reward": 0.00001693508780843028,
"accuracy": 0,
"format_score": 1,
"len": 10614,
"finish": "stop",
"closed_think": true
},
{
"reward": 1,
"accuracy": 1,
"format_score": 1,
"len": 9305,
"finish": "stop",
"closed_think": true
},
{
"reward": 1,
"accura... |
29 | CycleCounting | 1 | [
{
"reward": 9.5367431640625e-7,
"accuracy": 0,
"format_score": 1,
"len": 12725,
"finish": "stop",
"closed_think": true
},
{
"reward": 1,
"accuracy": 1,
"format_score": 1,
"len": 11822,
"finish": "stop",
"closed_think": true
},
{
"reward": 0,
"accuracy"... |
30 | DoubleCrossCounting | 3 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0.21599999999999997,
"accuracy": 0,
"format_score": 1,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": -1,
"a... |
31 | DoubleCrossCounting | 60 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
32 | DoubleCrossCounting | 10 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0.125,
"accuracy": 0,
"format_score": 1,
"len": 13031,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
... |
33 | DoubleCrossCounting | 0 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
34 | DoubleCrossCounting | 0 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0,
"accuracy": 0,
... |
35 | DoubleCrossCounting | 4 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
36 | DoubleCrossCounting | 9 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": true
},
{
"reward": 0.08779149519890259,
"accuracy": 0,
"format_score": 1,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"a... |
37 | DoubleCrossCounting | 14 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
38 | DoubleCrossCounting | 1 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
39 | DoubleCrossCounting | 71 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": 0.00034924883560438216,
... |
40 | LASLaser | 5 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
41 | LASLaser | 6 | [
{
"reward": 1,
"accuracy": 1,
"format_score": 1,
"len": 14755,
"finish": "stop",
"closed_think": true
},
{
"reward": 1,
"accuracy": 1,
"format_score": 1,
"len": 15963,
"finish": "stop",
"closed_think": true
},
{
"reward": -1,
"accuracy": 0,
"format... |
42 | LASLaser | 5 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
43 | LASLaser | 5 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
44 | LASLaser | 6 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
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45 | LASLaser | 6 | [
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46 | LASLaser | 6 | [
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47 | LASLaser | 5 | [
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48 | LASLaser | 5 | [
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49 | LASLaser | 6 | [
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50 | LandAcquisition | 4527 | [
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51 | LandAcquisition | 4749 | [
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52 | LandAcquisition | 3007 | [
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53 | LandAcquisition | 5406 | [
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54 | LandAcquisition | 2473 | [
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55 | LandAcquisition | 3948 | [
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56 | LandAcquisition | 4363 | [
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57 | LandAcquisition | 3730 | [
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58 | LandAcquisition | 3658 | [
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59 | LandAcquisition | 3569 | [
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60 | LandformGenerationCounting | 288 | [
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61 | LandformGenerationCounting | 4800 | [
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62 | LandformGenerationCounting | 1890 | [
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63 | LandformGenerationCounting | 300 | [
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64 | LandformGenerationCounting | 6048 | [
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65 | LandformGenerationCounting | 6720 | [
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66 | LandformGenerationCounting | 6804 | [
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67 | LandformGenerationCounting | 6912 | [
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68 | LandformGenerationCounting | 720 | [
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69 | LandformGenerationCounting | 4704 | [
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70 | MatrixRMQCounting | 268233 | [
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71 | MatrixRMQCounting | 378210 | [
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72 | MatrixRMQCounting | 316883 | [
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73 | MatrixRMQCounting | 22176 | [
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74 | MatrixRMQCounting | 109932 | [
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75 | MatrixRMQCounting | 460256 | [
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76 | MatrixRMQCounting | 331285 | [
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77 | MatrixRMQCounting | 299701 | [
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78 | MatrixRMQCounting | 33258 | [
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79 | MatrixRMQCounting | 148176 | [
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80 | MaxDifferentGroupPairDivision | 1 2 4 5 6 7 | [
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81 | MaxDifferentGroupPairDivision | 0 2 3 5 6 7 | [
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82 | MaxDifferentGroupPairDivision | 2 3 4 6 7 | [
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83 | MaxDifferentGroupPairDivision | 0 2 3 4 5 | [
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84 | MaxDifferentGroupPairDivision | 1 4 5 6 | [
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85 | MaxDifferentGroupPairDivision | 1 3 6 | [
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86 | MaxDifferentGroupPairDivision | 0 1 3 5 6 7 | [
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87 | MaxDifferentGroupPairDivision | 0 1 2 3 5 | [
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88 | MaxDifferentGroupPairDivision | 1 5 6 7 | [
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89 | MaxDifferentGroupPairDivision | 1 3 6 | [
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90 | MinConversionToCycleCost | 9 | [
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91 | MinConversionToCycleCost | 3 | [
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92 | MinConversionToCycleCost | 6 | [
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93 | MinConversionToCycleCost | 26 | [
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94 | MinConversionToCycleCost | 2 | [
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95 | MinConversionToCycleCost | 8 | [
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96 | MinConversionToCycleCost | 1 | [
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97 | MinConversionToCycleCost | 3 | [
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98 | MinConversionToCycleCost | 27 | [
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},
{
"reward": -1,
"accuracy": 0,
... |
99 | MinConversionToCycleCost | 4 | [
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
"format_score": 0,
"len": 16384,
"finish": "length",
"closed_think": false
},
{
"reward": -1,
"accuracy": 0,
... |
End of preview.
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rose-tinker-s25-eval-rollouts
RLVE 16k 评测的完整 rollout(含模型输出全文), 用于诊断 Tinker Nemotron teacher 训 25 步后 pass@8 掉到基线以下(0.0611 vs 基线 0.1111)的原因。
三个文件, 同一评测口径(180 题, n=8, CTX 18432 / MAX_NEW 16384, temp 0.7 / top_p 0.9):
| 文件 | 模型 | pass@8 | 顶满16k | 闭合 |
|---|---|---|---|---|
baseline_untrained.jsonl |
Qwen3-1.7B 基线 | 0.1111 | 73.7% | 30.1% |
tinker_nemotron_s25.jsonl |
ROSE x Nemotron-120B@Tinker, step25 | 0.0611 | 91% | 7~12% |
rose_4bt_n2_s70.jsonl |
ROSE x 本地 4B-Thinking, step70(旧参 n2) | 0.1833 | 84.9% | 17.1% |
每行: {"idx", "env", "gt", "recs": [8 x {reward, accuracy, len, finish, closed_think}], "text": [8 x 输出全文]}
诊断结论(待复核): ROSE 的 teacher 从不教收尾(续写总被 1024 预算切断), 4B-T 同族 teacher 也把 闭合率从 30% 压到 17% 但推理增益盖过伤害; Nemotron 是跨族 模型, 在它词表里是碎片, 续写里永远不含 Qwen 式"闭合 think -> 给答案"的 模式, 25 步就把学生的收尾行为压没了(91% 的 rollout 推理到 16k 截断也不出答案)。
训练数据侧的对应样本(学生前缀 + Nemotron 补全全文): https://huggingface.co/datasets/SeanWang0027/rose-tinker-step1-rollouts
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