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 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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 71, 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.
Antidistillation Sampling — intermediate traces
Intermediate artifacts (teacher reasoning traces + run metadata) from
antidistillation sampling experiments with an
openai/gpt-oss-120b teacher. Shared for experiment reproduction.
Each experiment is a gzipped tarball with its own README.md inside describing the setup,
the file inventory, and the measured accuracies.
| archive | size | run | max gen length | what's in it |
|---|---|---|---|---|
exp7.tar.gz |
54 MB | 4k-context run, 2026-05-13 → 05-21 | 4096 | holdout traces (exp9's holdout set lives here) + 8 small probe runs + 4 full-scale sets (all degenerate — see its README) |
exp8.tar.gz |
16 MB | 8k-context run, 2026-05-14 → 05-20 | 8192 | holdout traces + one lam=0.04 training set (degenerate — see its README) |
exp9.tar.gz |
34 MB | 2k-context run, 2026-05-21 → 05-24 | 2048 | full lam sweep: 5 training-trace sets (lam 0.01→0.02) + 7 teacher evals (lam 0.0→0.3) |
Start with exp9 — it is the most recent and the only one with a usable lam sweep. It
has no holdout set of its own; the matching one is exp7/traces/holdout.parquet (their
gradients are byte-identical).
exp7 and exp8 are included for the context-length comparison and for the record, but
their full-scale training traces all collapse to ~0.001 accuracy with degenerate token
repetition. In exp7's case this happens even at lam=0.0001, where ADS is effectively off,
so it is more likely a generation bug than the defense working. Each package's README spells
this out per file.
Shared setup
| Role | Model |
|---|---|
| Teacher (protected) | openai/gpt-oss-120b, MXFP4 |
| Proxy student (ADS gradients) | openai/gpt-oss-20b |
| Downstream student (distillation target) | meta-llama/Llama-3.2-1B |
Datasets: Big-Math (training + holdout), GSM8K (teacher eval).
Sampling: tau=0.6, eps=1e-2, seed=42.
Usage
tar xzf exp9.tar.gz
python -c "
import pandas as pd
d = pd.read_parquet('exp9/traces/tau0.6_lam0.01_eps1.00e-02.parquet')
print(d.shape, list(d.columns))
print('teacher accuracy:', d.is_raw_correct.mean())
"
All parquet files share the schema problem, solution, trace, is_raw_correct, where
trace is the teacher's raw output in gpt-oss harmony format. Every trace file has a
matching .yaml config snapshot, and metadata/trace_registry.jsonl records the full
hyperparameter set (lam, tau, eps, seed, split, models, sample count) for each one.
Proxy-student gradients
The Stage-2 gradients used by ADS, computed with the openai/gpt-oss-20b proxy student.
Stored at the repo root rather than inside the tarballs, because two of the three runs share
one file:
| file | size | used by | computed from |
|---|---|---|---|
student_grads_exp7_exp9.pt |
3.6 GB | exp7 and exp9 (byte-identical) | exp7/traces/holdout.parquet |
student_grads_exp8.pt |
3.6 GB | exp8 | exp8/traces/holdout.parquet |
Do not mix them up — pairing a run with the wrong gradient file will silently produce wrong ADS behavior.
These are torch.save pickles, so loading them executes arbitrary code; load only from a
source you trust:
grads = torch.load("student_grads_exp7_exp9.pt", map_location="cpu")
You need them only to re-run ADS sampling itself. Reproducing the distillation or the accuracies reported in each package does not require them — the traces are already generated.
Not included
The sharded HuggingFace datasets directories sitting next to each parquet (same content,
larger), and distilled student checkpoints (none were produced for these three runs).
Verifying downloads
a79cb78c046fb842453553dd02a69a1326ba48dd4ebec0b2016cd1b589e2ec41 exp7.tar.gz
422a5e3a1a8f301c15b62fef7fbe51a8afedf70937faffa4898a308e9e71e9c3 exp8.tar.gz
d82e1ab3a53075ec37944936ea66badc5307a7549a4cb57da5099b72af0256bc exp9.tar.gz
09e00e592256745714197ef5c9e84e51ab297e22123679db35a2b73228a2329c student_grads_exp7_exp9.pt
4e815f25e6c8fc1ad08d1c97e61ac3634e28e53df880c5362b7b7ca7c423b131 student_grads_exp8.pt
Or verify everything at once with the included SHA256SUMS:
sha256sum -c SHA256SUMS
Provenance and licensing
- Problems and reference answers derive from Big-Math (CC-BY-4.0) and GSM8K (MIT) — attribution required for redistribution.
- Traces are outputs of
openai/gpt-oss-120b(Apache-2.0); the model license places no restriction on generated outputs. - Built with Llama: the surrounding pipeline distills into
meta-llama/Llama-3.2-1B.
The license: other tag reflects this mix — the underlying licenses above govern use.
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