Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<eval_id: string, hf_commit_hash: null, model_name: string, runs: list<item: struct<config: struct<max_budget: double, max_turns: int64, seed: null, temperature: double, timestamp: string, use_debugging_tools: bool>, eval_id: string, results: struct<budget_exceeded: bool, cached_tokens: int64, completion_tokens: int64, conversation_log: list<item: struct<arguments: struct<breakpoints: list<item: struct<file: string, lines: list<item: int64>>>, command: string, content: string, end: int64, expression: string, file: string, file_path: string, new_string: string, old_string: string, start: int64, test: string, name: string>, content: string, reasoning: string, result: string, role: string, tool_calls: list<item: struct<function: struct<arguments: string, name: string>, id: string, type: string>>, tool_name: string, turn: int64>>, error_message: null, execution_time: double, final_code: string, final_test_passed: bool, prompt_tokens: int64, success: bool, test_output: string, tool_calls_made: list<item: string>, total_cost: double, total_tokens: int64, turns_used: int64>, run_id: string>>, scenario_name: string> vs list<item: struct<budget_exceeded: bool, cached_tokens: int64, completion_tokens: int64, conversation_log: list<item: struct<arguments: struct<breakpoints: list<item: struct<file: string, lines: list<item: int64>>>, command: string, content: string, end: int64, expression: string, file: string, file_path: string, name: string, new_string: string, old_string: string, start: int64, test: string>, content: string, reasoning: string, result: string, role: string, tool_calls: list<item: struct<function: struct<arguments: string, name: string>, id: string, type: string>>, tool_name: string, turn: int64>>, error_message: null, execution_time: double, final_code: string, final_test_passed: bool, max_budget: double, max_turns: int64, model_name: string, prompt_tokens: int64, scenario_name: string, success: bool, test_output: string, tool_calls_made: list<item: string>, total_cost: double, total_tokens: int64, turns_used: int64, use_debugging_tools: bool>>
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/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
llm-debugger evaluation transcripts
Every turn of every evaluation episode behind the results reported in
llm-debugger.
This covers the arms the reported numbers rest on — the base model, the SFT initialisation, and the RL policies trained from it. Exploratory runs that no reported figure depends on are not included.
Layout
| path | what it is |
|---|---|
runs/base/ |
Qwen3-Coder-30B-A3B-Instruct, 8 runs on the 30-task test split |
runs/sft/ |
the SFT initialisation, 3 runs on the test split |
runs/rl-gate-arc/ |
the RL gate arc, policy v15 through v120 (3 runs each, 8 at v90) |
runs/rl-v90-test/ |
RL policy v90, 8 runs on the test split |
runs/rl-v90-val/ |
RL policy v90, 8 runs on the 40-task validation split |
runs/sft-trajectories/ |
the SFT model's evaluation trajectories, turn by turn |
runs/sft/ is the gate at policy v0, which carries a zero-initialised RFT
delta, so it measures the frozen SFT model exactly. runs/sft-trajectories/
holds those same three runs turn by turn — every tool call, every observation,
every edit — rather than as per-defect outcome records.
A trajectory is paired to its run by the eval_id both carry, not by the
timestamp in the filename: a run directory is named when the run is scheduled
and the eval_id is stamped when it starts, so matching on time shifts every
run by one.
Each run directory is one gzipped archive, because thousands of small JSON files
make a Hub repo unbrowsable and the download one request per file. Fields that
name the run directory, such as run_dir in the per-task summary, are rewritten
to the published name so they resolve against what you extracted.
manifest.json lists every run with its file count and uncompressed size.
tar -xzf runs/rl-v90-val/v90_val_a_run1.tar.gz
Inside a run:
| file | what it is |
|---|---|
combined_results.json |
every episode's full conversation_log |
<model>.json |
per-defect run records for that arm |
evaluation_summary.json |
solve rate, turns, cost and tokens for the run |
per_task_summary.json |
per-defect outcome for the run |
Reading these honestly
Transcripts are model output. They contain wrong diagnoses, abandoned edits and
failed episodes — that is what they are for. Solve rates come from
final_test_passed, the suite passing after the episode, not from anything the
model claims about its own work.
Note that a trajectory file carries success, which is not the solve
metric. success requires the model to have called done; an episode that ran
to the turn cap with the suite passing has success: false and
final_test_passed: true. One of the 90 SFT trajectories published here is
exactly that case. Score from the run records in runs/sft/, and read the
trajectories for behaviour.
The gate arc is included because it is the evidence for a caveat rather than a
result. v90 was chosen by stop-at-peak on that arc, and the arc ran on the set
that is the pristine test split under defect_split_v2.json:
v0 64.4 | v15 65.6 | v30 63.3 | v45 70.0 | v60 62.2 | v75 72.2 |
v90 76.7 | v105 68.9 | v120 66.7
So v90's test figure is the argmax of nine noisy draws on the set it was then scored against, and the drop to 72.9 at 8 runs per arm is that winner's curse resolving. Cite the validation number, 75.9 / 93.1, which did not select the checkpoint.
Usage
from huggingface_hub import snapshot_download
path = snapshot_download("moofeez/llm-debugger-eval-transcripts", repo_type="dataset")
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