Dataset Viewer
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
model: string
benchmark: string
harness: string
runs: int64
tasks_per_run: int64
run_correct: struct<1: string>
child 0, 1: string
mean_accuracy: double
combined: string
time: double
campaign: string
sampling: struct<temperature: double, top_p: double, max_new_tokens: int64, note: string>
child 0, temperature: double
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, note: string
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
served_model_name: string
endpoint: string
agent: struct<name: string, harness: string, prompt: string, history_mode: string, hint_version: string, hi (... 136 chars omitted)
child 0, name: string
child 1, harness: string
child 2, prompt: string
child 3, history_mode: string
child 4, hint_version: string
child 5, hint_revisit_min: int64
child 6, hint_max_tried: int64
child 7, image_history: int64
child 8, loop_detect: int64
child 9, wait_after_action_seconds: double
child 10, note: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'harness': Value('string'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'harness': Value('string'), 'prompt': Value('string'), 'history_mode': Value('string'), 'hint_version': Value('string'), 'hint_revisit_min': Value('int64'), 'hint_max_tried': Value('int64'), 'image_history': Value('int64'), 'loop_detect': Value('int64'), 'wait_after_action_seconds': Value('float64'), 'note': Value('string')}, 'sampling': {'temperature': Value('float64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'note': Value('string')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
benchmark: string
harness: string
runs: int64
tasks_per_run: int64
run_correct: struct<1: string>
child 0, 1: string
mean_accuracy: double
combined: string
time: double
campaign: string
sampling: struct<temperature: double, top_p: double, max_new_tokens: int64, note: string>
child 0, temperature: double
child 1, top_p: double
child 2, max_new_tokens: int64
child 3, note: string
evaluation: struct<tasks: int64, apps: int64, parallel: int64, max_rounds: int64, task_class: string, docker_ima (... 63 chars omitted)
child 0, tasks: int64
child 1, apps: int64
child 2, parallel: int64
child 3, max_rounds: int64
child 4, task_class: string
child 5, docker_image: string
child 6, avd: string
child 7, seed_semantics: string
child 8, judge: string
served_model_name: string
endpoint: string
agent: struct<name: string, harness: string, prompt: string, history_mode: string, hint_version: string, hi (... 136 chars omitted)
child 0, name: string
child 1, harness: string
child 2, prompt: string
child 3, history_mode: string
child 4, hint_version: string
child 5, hint_revisit_min: int64
child 6, hint_max_tried: int64
child 7, image_history: int64
child 8, loop_detect: int64
child 9, wait_after_action_seconds: double
child 10, note: string
serving: struct<vllm: string, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, (... 120 chars omitted)
child 0, vllm: string
child 1, instance: struct<gpus: list<item: int64>, port: int64, tensor_parallel: int64, gpu_memory_utilization: double, (... 88 chars omitted)
child 0, gpus: list<item: int64>
child 0, item: int64
child 1, port: int64
child 2, tensor_parallel: int64
child 3, gpu_memory_utilization: double
child 4, max_model_len: int64
child 5, max_num_seqs: int64
child 6, seed: int64
child 7, extra_args: list<item: string>
child 0, item: string
to
{'model': Value('string'), 'served_model_name': Value('string'), 'harness': Value('string'), 'serving': {'vllm': Value('string'), 'instance': {'gpus': List(Value('int64')), 'port': Value('int64'), 'tensor_parallel': Value('int64'), 'gpu_memory_utilization': Value('float64'), 'max_model_len': Value('int64'), 'max_num_seqs': Value('int64'), 'seed': Value('int64'), 'extra_args': List(Value('string'))}}, 'agent': {'name': Value('string'), 'harness': Value('string'), 'prompt': Value('string'), 'history_mode': Value('string'), 'hint_version': Value('string'), 'hint_revisit_min': Value('int64'), 'hint_max_tried': Value('int64'), 'image_history': Value('int64'), 'loop_detect': Value('int64'), 'wait_after_action_seconds': Value('float64'), 'note': Value('string')}, 'sampling': {'temperature': Value('float64'), 'top_p': Value('float64'), 'max_new_tokens': Value('int64'), 'note': Value('string')}, 'evaluation': {'tasks': Value('int64'), 'apps': Value('int64'), 'parallel': Value('int64'), 'max_rounds': Value('int64'), 'task_class': Value('string'), 'docker_image': Value('string'), 'avd': Value('string'), 'seed_semantics': Value('string'), 'judge': Value('string')}, 'endpoint': Value('string'), 'campaign': Value('string'), 'time': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MobileGym-4B — Android Lab
One run of the MobileGUI-Gym MBGym harness (GenericV3 protocol + hint v2 ScreenMemory, loop_detect 8) with the MobileGym-4B checkpoint (Qwen3-VL 4B SFT) on Android Lab (138 tasks, 9 apps), 25 rounds max, parallel 8. The harness was ported to Android-Lab for this evaluation. Sampling: MobileGUI-Gym run_train.sh validation config (temperature 0.1, top_p 1.0, max_new_tokens 1024). Judge: gemini-3-flash for query tasks; rule-based for operation tasks.
| Run | Correct |
|---|---|
| 1 | 52/138 |
Accuracy: 52/138 = 37.68%.
run_1/config.yaml— eval config (agent, sampling, docker image, AVD).run_1/scores/—results.jsonl(per-task judge/rule outcome) andtotal.jsonl.run_1/trajectories.tar.gz— native traces (per-step prompts/screenshots/actions, incl. hint_v2 notes and ScreenMemory stats).two_runs/— combined episodes, protocol, summary, package manifest.
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