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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
pass@1: double
pass@1_ci95: list<item: double>
child 0, item: double
pass@1_sd_boot: double
pass@1_by_rollout_index: list<item: double>
child 0, item: double
pass@1_sd_rollouts: double
pass@8: double
pass@8_ci95: list<item: double>
child 0, item: double
pass@8_sd_boot: double
pass@16: double
pass@16_ci95: list<item: double>
child 0, item: double
pass@16_sd_boot: double
cap_rate: double
n_problems: int64
n_rollouts: int64
rollouts_per_problem: int64
max_tokens_used: list<item: int64>
child 0, item: int64
prompt_style: list<item: null>
child 0, item: null
median_output_tokens: int64
mean_output_tokens: double
expected_problems: int64
complete: bool
files: list<item: string>
child 0, item: string
per_problem: struct<aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list (... 8176 chars omitted)
child 0, aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list<item: int64>, answ (... 161 chars omitted)
child 0, rollout_index: list<item: int64>
child 0, item: int64
child 1, correct: list<item: int64>
child 0, item: int64
child 2, tokens: list<item: int64>
child 0, item: int64
child 3, answer: list<item: string>
child 0, item: string
child 4, hit_cap: list<item: int64>
child 0, item: int64
child 5, answer_end_frac: list<item: double>
child 0, item: double
child 6, answer_marker: list<item: s
...
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 27, 28: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 28, 29: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 29, 30: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 30, 31: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 31, 32: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
aggregate_by_k_doc: string
problem_key: string
steer: string
committed: bool
correct: bool
completion: string
label: string
output_tokens: int64
hit_token_cap: bool
finish_reason: string
rollout_index: int64
to
{'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
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
pass@1: double
pass@1_ci95: list<item: double>
child 0, item: double
pass@1_sd_boot: double
pass@1_by_rollout_index: list<item: double>
child 0, item: double
pass@1_sd_rollouts: double
pass@8: double
pass@8_ci95: list<item: double>
child 0, item: double
pass@8_sd_boot: double
pass@16: double
pass@16_ci95: list<item: double>
child 0, item: double
pass@16_sd_boot: double
cap_rate: double
n_problems: int64
n_rollouts: int64
rollouts_per_problem: int64
max_tokens_used: list<item: int64>
child 0, item: int64
prompt_style: list<item: null>
child 0, item: null
median_output_tokens: int64
mean_output_tokens: double
expected_problems: int64
complete: bool
files: list<item: string>
child 0, item: string
per_problem: struct<aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list (... 8176 chars omitted)
child 0, aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list<item: int64>, answ (... 161 chars omitted)
child 0, rollout_index: list<item: int64>
child 0, item: int64
child 1, correct: list<item: int64>
child 0, item: int64
child 2, tokens: list<item: int64>
child 0, item: int64
child 3, answer: list<item: string>
child 0, item: string
child 4, hit_cap: list<item: int64>
child 0, item: int64
child 5, answer_end_frac: list<item: double>
child 0, item: double
child 6, answer_marker: list<item: s
...
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 27, 28: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 28, 29: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 29, 30: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 30, 31: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
child 31, 32: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
child 0, k: int64
child 1, expected_tokens: double
child 2, pass_at_k: double
child 3, majority_at_k: double
aggregate_by_k_doc: string
problem_key: string
steer: string
committed: bool
correct: bool
completion: string
label: string
output_tokens: int64
hit_token_cap: bool
finish_reason: string
rollout_index: int64
to
{'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
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.
problem_key string | rollout_index int64 | arm string | steer string | label string | committed bool | correct bool | hit_token_cap bool | finish_reason string | output_tokens int64 | max_tokens_used int64 | completion string |
|---|---|---|---|---|---|---|---|---|---|---|---|
aime2024_0 | 0 | none | base | run | true | true | false | stop | 1,076 | 31,744 | To solve this problem, we need to determine the time it takes Aya to walk 9 kilometers at different speeds, including the time spent in the coffee shop. Let's break it down step by step.
### Step 1: Define the variables
- Let \( s \) be the constant speed in kilometers per hour.
- Let \( t \) be the time spent in the... |
aime2024_0 | 1 | none | base | run | false | false | true | length | 31,744 | 31,744 | To solve this problem, we need to determine the time it takes for Aya to walk 9 kilometers at a speed of \( s + \frac{1}{2} \) kilometers per hour, including the time spent in the coffee shop.
First, let's set up the equations based on the information given:
1. **First Scenario:**
- Speed: \( s \) km/h
- Time:... |
aime2024_0 | 2 | none | base | run | true | true | false | stop | 1,783 | 31,744 | To solve this problem, we need to determine the walking speed \( s \) and the time \( t \) spent in the coffee shop. Let's start by setting up the equations based on the given information.
1. **First scenario:**
- Distance: 9 km
- Speed: \( s \) km/h
- Total time: 4 hours
- Time spent walking: \( 4 \text{... |
aime2024_0 | 3 | none | base | run | true | true | false | stop | 1,458 | 31,744 | To solve the problem, let's break it down step by step.
### Step 1: Define the variables
- Let \( s \) be Aya's walking speed in kilometers per hour.
- Let \( t \) be the time spent in the coffee shop in minutes.
- The total walking distance is \( 9 \) kilometers.
### Step 2: Analyze the first scenario
When Aya walk... |
aime2024_0 | 4 | none | base | run | true | true | false | stop | 1,540 | 31,744 | To solve this problem, let's break it down step by step.
### Step 1: Understand the given information
1. **First Scenario:**
- **Speed:** \( s \) km/h
- **Total time:** 4 hours
- **Walk time:** \( 4 \) hours \( - t \) minutes
2. **Second Scenario:**
- **Speed:** \( s + 2 \) km/h
- **Total time:** 2 h... |
aime2024_0 | 5 | none | base | run | true | true | false | stop | 1,745 | 31,744 | To solve the problem, let's break it down step by step.
### Step 1: Understand the given information
1. **First scenario**:
- Speed: \( s \) km/h
- Time spent walking: \( 4 \) hours
- Time spent in the coffee shop: \( t \) minutes
- Total time: \( 4 \) hours \( + t \) minutes \( = \frac{240 + t}{60} \) ho... |
aime2024_0 | 6 | none | base | run | true | true | false | stop | 1,419 | 31,744 | To solve this problem, we need to determine the time Aya spends walking and then add the time she spends in the coffee shop.
Let's start by setting up the equations based on the information given:
1. When Aya walks at \( s \) kilometers per hour, the total time taken is 4 hours, including \( t \) minutes spent in th... |
aime2024_0 | 7 | none | base | run | true | true | false | stop | 1,045 | 31,744 | To solve this problem, let's break it down step by step.
### Step 1: Understand the problem
Aya walks 9 kilometers every morning. We are given two scenarios:
1. When she walks at a speed of \( s \) km/h, the total time (walking + coffee shop) is 4 hours, including \( t \) minutes in the coffee shop.
2. When she walks... |
aime2024_0 | 8 | none | base | run | true | false | false | stop | 944 | 31,744 | To solve this problem, we need to determine the values of \( s \) and \( t \) first, and then use these to find the time it takes for Aya to walk at \( s + \frac{1}{2} \) kilometers per hour.
### Step 1: Determine \( s \) and \( t \)
#### First Scenario:
When Aya walks at \( s \) kilometers per hour, the walk takes ... |
aime2024_0 | 9 | none | base | run | true | true | false | stop | 1,458 | 31,744 | To solve the problem, we need to determine the time Aya spends walking and the time she spends in the coffee shop. Let's start by setting up the equations based on the given information.
First, let's denote the time spent walking at speed \( s \) kilometers per hour as \( T_1 \) and the time spent walking at speed \(... |
aime2024_0 | 10 | none | base | run | false | false | true | length | 31,744 | 31,744 | To solve the problem, we need to determine the time it takes Aya to walk 9 kilometers at a speed of \( s + \frac{1}{2} \) kilometers per hour, including the time spent in the coffee shop.
First, let's set up the equations based on the given information.
1. **First scenario:**
- Speed: \( s \) km/h
- Time walki... |
aime2024_0 | 11 | none | base | run | true | true | false | stop | 1,046 | 31,744 | To solve this problem, we need to determine the time it takes for Aya to walk 9 kilometers at a speed of \( s + \frac{1}{2} \) kilometers per hour, including the time spent in the coffee shop.
First, let's find the speed \( s \) and the time spent in the coffee shop \( t \).
### Step 1: Determine the walking time wi... |
aime2024_0 | 12 | none | base | run | true | false | false | stop | 1,275 | 31,744 | To solve this problem, we need to determine the time it takes Aya to walk 9 kilometers at different speeds, including the time spent in the coffee shop. Let's break down the problem step by step.
### Step 1: Define the Variables
- Let \( s \) be Aya's walking speed in kilometers per hour (km/h).
- Let \( t \) be the ... |
aime2024_0 | 13 | none | base | run | true | false | false | stop | 951 | 31,744 | To solve the problem, we need to determine the walking speed \(s\) and the time spent in the coffee shop \(t\). Then, we will calculate the time it takes for Aya to walk 9 kilometers at the speed \(s + \frac{1}{2}\) kilometers per hour, including the time spent in the coffee shop.
### Step 1: Determine the walking sp... |
aime2024_0 | 14 | none | base | run | true | true | false | stop | 4,723 | 31,744 | Let's solve the problem step by step.
### Step 1: Understand the given information
1. **First Scenario:**
- Speed: \( s \) km/h
- Time: 4 hours
- Distance: 9 km
- Time spent in coffee shop: \( t \) minutes
2. **Second Scenario:**
- Speed: \( s + 2 \) km/h
- Time: 2 hours and 24 minutes
- Distan... |
aime2024_0 | 15 | none | base | run | true | true | false | stop | 1,564 | 31,744 | To solve this problem, we need to determine the time it takes for Aya to walk 9 kilometers at a speed of \( s + \frac{1}{2} \) kilometers per hour, including the time spent in the coffee shop.
First, let's find the walking speed \( s \) when Aya walks at \( s \) kilometers per hour for 4 hours, including \( t \) minu... |
aime2024_1 | 0 | none | base | run | true | false | false | stop | 1,410 | 31,744 | To solve the problem, we will use properties of cyclic quadrilaterals, power of a point, and the Angle Bisector Theorem.
First, let's denote the points and lengths given in the problem:
- \(ABC\) is a triangle inscribed in circle \(\omega\).
- The tangents to \(\omega\) at \(B\) and \(C\) intersect at point \(D\).
- ... |
End of preview.
rollouts-qwen4b-rl — evaluation rollouts
Model: amildravid4292/Qwen3-4B-Base-GRPO-MATH-ck300. Tokenizer used for answer positions: Qwen/Qwen3-4B-Base.
Protocol: 32 rollouts per problem (two seeded batches of 16), temperature 0.6, top-p 0.95,
budget 31,744 generated tokens, seed 20260819. Prompts and grader: the paper's repository
(sophicle/reason). Rollout jsonl files are kept as written (one graded rollout per line, with the
completion), under the run directories as on disk (run/, run_regraded/, *_execgraded/);
cell summaries are at <prompt>/<benchmark>/summary/<arm>.json.
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