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Datasets:
AIGNLAI
/
RouteStep

Tasks:
Reinforcement Learning
Text Classification
Languages:
English
Tags:
agent-routing
appworld
scienceworld
multi-model-routing
License:
Dataset card Files
xet
 Community
Dataset Viewer
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema_version: string
currency: string
unit: string
cache_mode: string
cny_per_usd: double
models: list<item: struct<slug: string, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_w (... 31 chars omitted)
  child 0, item: struct<slug: string, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_write: doubl (... 19 chars omitted)
      child 0, slug: string
      child 1, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_write: double, output: double>
          child 0, input: double
          child 1, cache_read: double
          child 2, cache_write: double
          child 3, output: double
pricing_file: string
mixed_trajectories: struct<directory: string, count: int64, successful: int64, cost_complete: int64, total_steps: int64, (... 114 chars omitted)
  child 0, directory: string
  child 1, count: int64
  child 2, successful: int64
  child 3, cost_complete: int64
  child 4, total_steps: int64
  child 5, anchor_total_estimated_cost_usd: double
  child 6, mixed_total_estimated_cost_usd: double
  child 7, savings_vs_anchor_ratio: double
cost_method: string
selected_model_counts: struct<deepseek_v31_terminus: int64, gemini3_flash_preview: int64, glm45_air: int64, glm52: int64, g (... 70 chars omitted)
  child 0, deepseek_v31_terminus: int64
  child 1, gemini3_flash_preview: int64
  child 2, glm45_air: int64
  child 3, glm52: int64
  child 4, gpt54_nano: int64
  child 5, gpt5_mini: int64
  child 6, haiku45: int64
  child 7, llama31_8b: int64
single_model_trajectories: struct<directory: string, count: int64, successful: int64, total_estimated_cost_usd: double>
  child 0, directory: string
  child 1, count: int64
  child 2, successful: int64
  child 3, total_estimated_cost_usd: double
task_ids_sha256: string
to
{'schema_version': Value('string'), 'pricing_file': Value('string'), 'cost_method': Value('string'), 'single_model_trajectories': {'directory': Value('string'), 'count': Value('int64'), 'successful': Value('int64'), 'total_estimated_cost_usd': Value('float64')}, 'mixed_trajectories': {'directory': Value('string'), 'count': Value('int64'), 'successful': Value('int64'), 'cost_complete': Value('int64'), 'total_steps': Value('int64'), 'anchor_total_estimated_cost_usd': Value('float64'), 'mixed_total_estimated_cost_usd': Value('float64'), 'savings_vs_anchor_ratio': Value('float64')}, 'selected_model_counts': {'deepseek_v31_terminus': Value('int64'), 'gemini3_flash_preview': Value('int64'), 'glm45_air': Value('int64'), 'glm52': Value('int64'), 'gpt54_nano': Value('int64'), 'gpt5_mini': Value('int64'), 'haiku45': Value('int64'), 'llama31_8b': Value('int64')}, 'task_ids_sha256': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in 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 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
              schema_version: string
              currency: string
              unit: string
              cache_mode: string
              cny_per_usd: double
              models: list<item: struct<slug: string, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_w (... 31 chars omitted)
                child 0, item: struct<slug: string, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_write: doubl (... 19 chars omitted)
                    child 0, slug: string
                    child 1, usd_per_1m_tokens: struct<input: double, cache_read: double, cache_write: double, output: double>
                        child 0, input: double
                        child 1, cache_read: double
                        child 2, cache_write: double
                        child 3, output: double
              pricing_file: string
              mixed_trajectories: struct<directory: string, count: int64, successful: int64, cost_complete: int64, total_steps: int64, (... 114 chars omitted)
                child 0, directory: string
                child 1, count: int64
                child 2, successful: int64
                child 3, cost_complete: int64
                child 4, total_steps: int64
                child 5, anchor_total_estimated_cost_usd: double
                child 6, mixed_total_estimated_cost_usd: double
                child 7, savings_vs_anchor_ratio: double
              cost_method: string
              selected_model_counts: struct<deepseek_v31_terminus: int64, gemini3_flash_preview: int64, glm45_air: int64, glm52: int64, g (... 70 chars omitted)
                child 0, deepseek_v31_terminus: int64
                child 1, gemini3_flash_preview: int64
                child 2, glm45_air: int64
                child 3, glm52: int64
                child 4, gpt54_nano: int64
                child 5, gpt5_mini: int64
                child 6, haiku45: int64
                child 7, llama31_8b: int64
              single_model_trajectories: struct<directory: string, count: int64, successful: int64, total_estimated_cost_usd: double>
                child 0, directory: string
                child 1, count: int64
                child 2, successful: int64
                child 3, total_estimated_cost_usd: double
              task_ids_sha256: string
              to
              {'schema_version': Value('string'), 'pricing_file': Value('string'), 'cost_method': Value('string'), 'single_model_trajectories': {'directory': Value('string'), 'count': Value('int64'), 'successful': Value('int64'), 'total_estimated_cost_usd': Value('float64')}, 'mixed_trajectories': {'directory': Value('string'), 'count': Value('int64'), 'successful': Value('int64'), 'cost_complete': Value('int64'), 'total_steps': Value('int64'), 'anchor_total_estimated_cost_usd': Value('float64'), 'mixed_total_estimated_cost_usd': Value('float64'), 'savings_vs_anchor_ratio': Value('float64')}, 'selected_model_counts': {'deepseek_v31_terminus': Value('int64'), 'gemini3_flash_preview': Value('int64'), 'glm45_air': Value('int64'), 'glm52': Value('int64'), 'gpt54_nano': Value('int64'), 'gpt5_mini': Value('int64'), 'haiku45': Value('int64'), 'llama31_8b': Value('int64')}, 'task_ids_sha256': Value('string')}
              because column names don't match

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RouteStep Trajectory Dataset

This directory contains normalized AppWorld and ScienceWorld trajectories for step-level model routing. Every JSON file is self-contained and uses USD cache-aware token-price estimates from pricing.json.

Layout 

dataset/
├── pricing.json
├── appworld/
│   ├── manifest.json
│   ├── task_ids.txt
│   ├── single_model_trajectories/<task_id>/<model_slug>.json
│   └── mixed_trajectories/<task_id>.json
└── scienceworld/
    ├── manifest.json
    ├── task_ids.txt
    ├── single_model_trajectories/<task_id>/<model_slug>.json
    └── mixed_trajectories/<task_id>.json

Cost convention

prompt_tokens includes cached tokens. For every model call:

uncached_input_tokens = prompt_tokens - cache_read_tokens - cache_write_tokens
estimated_cost_usd = (
    uncached_input_tokens * input_price
    + cache_read_tokens * cache_read_price
    + cache_write_tokens * cache_write_price
    + completion_tokens * output_price
) / 1_000_000

Provider-reported spend is not used for cross-model comparisons. Prices and all costs are denominated in USD.

In AppWorld single-model files, state.history_step_indices references earlier steps in the same trajectory instead of duplicating their complete action and observation payloads. This preserves the full ordered history while keeping the dataset compact.

Coverage

  • AppWorld: 3,616 single-model trajectories and 418 successful mixed trajectories.
  • ScienceWorld: 1,525 single-model trajectories and 477 successful mixed trajectories.
  • All AppWorld mixed trajectories have complete per-step usage and cost fields.
  • 299 ScienceWorld mixed trajectories have complete per-step usage and costs. The remaining 178 historical trajectories did not retain candidate-call token usage; their unavailable usage and cost fields are null rather than inferred.

See each benchmark's manifest.json for aggregate counts and costs.

Downloaded archives

The Hugging Face release stores each benchmark as a compressed archive to avoid per-file API rate limits. Each archive expands to the layout documented above:

tar --zstd -xf appworld.tar.zst
tar --zstd -xf scienceworld.tar.zst
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