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
source: string
destination: string
type: string
status: string
service: null
agent: struct<name: string, version: string, model_name: string, extra: struct<parser: string, llm_kwargs: (... 25 chars omitted)
child 0, name: string
child 1, version: string
child 2, model_name: string
child 3, extra: struct<parser: string, llm_kwargs: struct<api_key: string>>
child 0, parser: string
child 1, llm_kwargs: struct<api_key: string>
child 0, api_key: string
steps: list<item: struct<step_id: int64, timestamp: string, source: string, message: string, model_name: st (... 323 chars omitted)
child 0, item: struct<step_id: int64, timestamp: string, source: string, message: string, model_name: string, reaso (... 311 chars omitted)
child 0, step_id: int64
child 1, timestamp: string
child 2, source: string
child 3, message: string
child 4, model_name: string
child 5, reasoning_content: string
child 6, tool_calls: list<item: struct<tool_call_id: string, function_name: string, arguments: struct<keystrokes: string, (... 20 chars omitted)
child 0, item: struct<tool_call_id: string, function_name: string, arguments: struct<keystrokes: string, duration: (... 8 chars omitted)
child 0, tool_call_id: string
child 1, function_name: string
child 2, arguments: struct<keystrokes: string, duration: double>
child 0, keystrokes: string
child 1, duration: double
child 7, observation: struct<results: list<item: struct<source_call_id: string, content: string>>>
child 0, results: list<item: struct<source_call_id: string, content: string>>
child 0, item: struct<source_call_id: string, content: string>
child 0, source_call_id: string
child 1, content: string
child 8, metrics: struct<prompt_tokens: int64, completion_tokens: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
session_id: string
final_metrics: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_cached_tokens: int64>
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_cached_tokens: int64
schema_version: string
to
{'schema_version': Value('string'), 'session_id': Value('string'), 'agent': {'name': Value('string'), 'version': Value('string'), 'model_name': Value('string'), 'extra': {'parser': Value('string'), 'llm_kwargs': {'api_key': Value('string')}}}, 'steps': List({'step_id': Value('int64'), 'timestamp': Value('string'), 'source': Value('string'), 'message': Value('string'), 'model_name': Value('string'), 'reasoning_content': Value('string'), 'tool_calls': List({'tool_call_id': Value('string'), 'function_name': Value('string'), 'arguments': {'keystrokes': Value('string'), 'duration': Value('float64')}}), 'observation': {'results': List({'source_call_id': Value('string'), 'content': Value('string')})}, 'metrics': {'prompt_tokens': Value('int64'), 'completion_tokens': Value('int64')}}), 'final_metrics': {'total_prompt_tokens': Value('int64'), 'total_completion_tokens': Value('int64'), 'total_cached_tokens': Value('int64')}}
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
source: string
destination: string
type: string
status: string
service: null
agent: struct<name: string, version: string, model_name: string, extra: struct<parser: string, llm_kwargs: (... 25 chars omitted)
child 0, name: string
child 1, version: string
child 2, model_name: string
child 3, extra: struct<parser: string, llm_kwargs: struct<api_key: string>>
child 0, parser: string
child 1, llm_kwargs: struct<api_key: string>
child 0, api_key: string
steps: list<item: struct<step_id: int64, timestamp: string, source: string, message: string, model_name: st (... 323 chars omitted)
child 0, item: struct<step_id: int64, timestamp: string, source: string, message: string, model_name: string, reaso (... 311 chars omitted)
child 0, step_id: int64
child 1, timestamp: string
child 2, source: string
child 3, message: string
child 4, model_name: string
child 5, reasoning_content: string
child 6, tool_calls: list<item: struct<tool_call_id: string, function_name: string, arguments: struct<keystrokes: string, (... 20 chars omitted)
child 0, item: struct<tool_call_id: string, function_name: string, arguments: struct<keystrokes: string, duration: (... 8 chars omitted)
child 0, tool_call_id: string
child 1, function_name: string
child 2, arguments: struct<keystrokes: string, duration: double>
child 0, keystrokes: string
child 1, duration: double
child 7, observation: struct<results: list<item: struct<source_call_id: string, content: string>>>
child 0, results: list<item: struct<source_call_id: string, content: string>>
child 0, item: struct<source_call_id: string, content: string>
child 0, source_call_id: string
child 1, content: string
child 8, metrics: struct<prompt_tokens: int64, completion_tokens: int64>
child 0, prompt_tokens: int64
child 1, completion_tokens: int64
session_id: string
final_metrics: struct<total_prompt_tokens: int64, total_completion_tokens: int64, total_cached_tokens: int64>
child 0, total_prompt_tokens: int64
child 1, total_completion_tokens: int64
child 2, total_cached_tokens: int64
schema_version: string
to
{'schema_version': Value('string'), 'session_id': Value('string'), 'agent': {'name': Value('string'), 'version': Value('string'), 'model_name': Value('string'), 'extra': {'parser': Value('string'), 'llm_kwargs': {'api_key': Value('string')}}}, 'steps': List({'step_id': Value('int64'), 'timestamp': Value('string'), 'source': Value('string'), 'message': Value('string'), 'model_name': Value('string'), 'reasoning_content': Value('string'), 'tool_calls': List({'tool_call_id': Value('string'), 'function_name': Value('string'), 'arguments': {'keystrokes': Value('string'), 'duration': Value('float64')}}), 'observation': {'results': List({'source_call_id': Value('string'), 'content': Value('string')})}, 'metrics': {'prompt_tokens': Value('int64'), 'completion_tokens': Value('int64')}}), 'final_metrics': {'total_prompt_tokens': Value('int64'), 'total_completion_tokens': Value('int64'), 'total_cached_tokens': Value('int64')}}
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.
Tokenwave TB-dev trajectories
Harbor Terminus-2 trials of Qwen3.5-9B checkpoints on OpenThoughts-TB-dev (70 tasks x 8 trials, 64k context, summarize on, no turn cap, temperature as in the eval config).
tbdev/K_teacher_reply_x0.375_iter67/
- Model: Qwen3.5-9B after 68 async GRPO updates (32 x 8) with teacher intervention, arm "K": the teacher rewrites the first wrong turn of a failed trial, the policy continues; a fixed trajectory is added to its group. Trained: the teacher's reply at step weight 0.375 (its thinking and the replayed prefix not trained). Run
ivtw1105, eval job 3807008. - Score: lenient 53.36% / strict 51.83% (baseline GRPO 32 x 8 at the same step: 53.29% / 52.40%).
- Each trial dir:
agent/trajectory.json(full multi-turn conversation incl. thinking),agent/trajectory.summarization-*(context summaries),agent/terminus_2.pane,verifier/(test output, reward),result.json,config.json,trial.log. - API keys in configs are redacted.
AKIAIOSFODNN7EXAMPLEin one task is AWS's documented example key, part of the task data.
tbdev/GRPO_default_32x8_iter67/
- Model: Qwen3.5-9B after 68 async GRPO updates (32 x 8, staleness <= 2), the default setting, no teacher. Run
ag32x8_1102, eval job 3767729. The baseline for K above (same step, same eval). - Score: lenient 53.29% / strict 52.40%. Same file layout as above. Fake credentials such as
sk-secret-.../sekret-key-...are task data.
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