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/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/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.
NEXUS Engine Testing Release: Premium Chess RL Trace
Format: Compressed Tarball containing JSON Lines (.jsonl) — one record per line
Records: 475,989
Tier: premium
License: CC0 1.0 — public domain
Generator: NEXUS Engine v1.0
What this is
This is an early testing release from the NEXUS Engine. We are releasing this dataset to demonstrate our capability to capture, filter, and structure high-quality human decision data for AI alignment and behavioral modeling.
Each record is a single human decision, encoded as (state, action, reward, next_state). The dataset is drop-in compatible with standard supervised-learning and RL training pipelines (PyTorch Dataset, Hugging Face datasets, JAX, etc.).
It is built specifically for Behavioral Cloning, Reinforcement Learning from Human Feedback (RLHF), and training Foundation Models on cognitive planning and spatial reasoning.
Quality filters applied
- tier = premium
- min_elo = 1800 (Ensures high-quality, expert-level strategic data)
- max_position_freq = 100 (Prevents standard opening-book memorization bias)
Pipeline yield
| Stage | Count | Retention |
|---|---|---|
| Raw tuples ingested | 475,989 | 100.0% |
| Passed quality filter | 475,989 | 100.0% |
| Successfully projected | 475,989 | 100.0% |
| Final retained after dedup | 475,989 | 100.0% |
Sample record
{
"state": "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
"action": "e4",
"uci": "e2e4",
"next_state": "rnbqkbnr/pppppppp/8/8/4P3/8/PPPP1PPP/RNBQKBNR b KQkq - 0 1",
"side_to_move": "white",
"elo": 2400,
"rating": null,
"reward": 0.0
}
Suggested usage
Extract the .tar.gz and load the .jsonl directly into your training pipeline:
import json
import tarfile
with tarfile.open("chess_premium_v1.tar.gz", "r:gz") as tar:
jsonl_file = tar.extractfile("lichess_chess__rl_trace__premium/dataset.jsonl")
records = [json.loads(line) for line in jsonl_file]
print(f"Loaded {len(records):,} premium RL traces")
Why NEXUS data is different
Unlike paid-annotator datasets (Mechanical Turk, Surge AI, Scale AI), every record here came from a human who was voluntarily engaged in a task they chose to do. There is no satisficing bias from rushing for piecework pay, no fatigue degradation from long annotation sessions, and no synthetic-prompt artifacts. The decisions are genuine cognitive signals.
Citation
If you use this dataset in research, please cite both the original public source (Lichess Open Database) and the NEXUS Engine used to filter and structure it.
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