Datasets:

kailinjiang
/

AdaTutoRank-Train-Data

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 791, in read_json
                  json_reader = JsonReader(
                      path_or_buf,
                  ...<16 lines>...
                      engine=engine,
                  )
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 905, in __init__
                  self.data = self._preprocess_data(data)
                              ~~~~~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 917, in _preprocess_data
                  data = data.read()
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbc in position 41: invalid start byte
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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AdaTutoRank Training Data

Training data for AdaTutoRank, a setwise reranker that selects a set of documents by joint utility rather than scoring documents independently by relevance.

File	Rows	Stage
`AdaTutoRank-SFT-Data.json`	8,958	Stage 1 — supervised cold start
`AdaTutoRank-ATO-Data-Train.parquet`	3,557	Stage 2 — Adaptive Tutoring Optimization, train
`AdaTutoRank-ATO-Data-Val.parquet`	100	Stage 2 — Adaptive Tutoring Optimization, validation

`AdaTutoRank-SFT-Data.json`

A JSON array of objects with three string fields.

Field	Content
`system`	System prompt defining the document-set selector role and the meta-rubric
`instruction`	User prompt: the search query, its intent, and the numbered candidate documents
`output`	The selected document identifiers, e.g. `[1] [2] [3] [5] [11] [18]`

`AdaTutoRank-ATO-Data-*.parquet`

Both splits share the same schema. Five top-level columns:

Field	Type	Content
`prompt`	`list<struct<role, content>>`	Chat-formatted messages given to the policy
`data_source`	`string`	Source dataset identifier
`ability`	`string`	Task type
`reward_model`	`struct<style, ground_truth>`	Reward style and ground truth for scoring
`extra_info`	`struct`	Per-query metadata, expanded below

extra_info holds seven sub-fields:

Field	Type	Content
`split`	`string`	`train` or `val`
`index`	`int64`	Query index
`initial_list`	`list<struct<content>>`	The candidate documents to select from
`rubrics`	`struct`	Query-specific rubrics, expanded below
`scenario`	`string`	`rag` or `deepresearch`
`query`	`string`	The search query
`thinking`	`string`	Reasoning that produced the query intent

extra_info.rubrics is a three-level hierarchy of nine dimensions. Each dimension is a list<struct<description: string, weight: int64>>, where description states a criterion the optimal document set should satisfy and weight is its importance.

Group	Dimensions
`doc_level_rubrics`	`Relevance`, `Authenticity`, `Quality`
`set_level_rubrics`	`Complementarity`, `Redundancy`, `Conflict`
`global_level_rubrics`	`Completeness`, `Density`, `Reachability`

Usage

from datasets import load_dataset

sft       = load_dataset("kailinjiang/AdaTutoRank-Train-Data", "sft", split="train")
ato_train = load_dataset("kailinjiang/AdaTutoRank-Train-Data", "ato", split="train")
ato_val   = load_dataset("kailinjiang/AdaTutoRank-Train-Data", "ato", split="validation")

Notes

Training-time supervision signals are withheld from the released ATO splits: the reference optimal document set and its judge score, together with the provenance and reward bookkeeping used while building them. Everything else is unmodified.

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