caiyuchen/OPV-Science
OPV Science: original experiment data Prepared for Learning to Steer, Steering to See. Original four-domain split (physics, chemistry, biology, material). Original system/user prompts and answer-tag instructions are preserved. Split Rows train 3,901 validation 434 Provenance and terms The data is derived from the following sources; their original terms and attribution obligations continue to apply. No new blanket license is asserted over the… See the full description on the dataset page: https://huggingface.co/datasets/caiyuchen/OPV-Science.
OPV Science: original experiment data
Prepared for Learning to Steer, Steering to See. Original four-domain split (physics, chemistry, biology, material). Original system/user prompts and answer-tag instructions are preserved.
Provenance and terms
The data is derived from the following sources; their original terms and attribution obligations continue to apply. No new blanket license is asserted over the collection. Source-file and uploaded-file hashes are in manifest.json.
- https://huggingface.co/datasets/hicai-zju/SciKnowEval
Format and evaluation
These are byte-for-byte copies of the original Parquet files. All columns, Arrow schemas, nested verifier metadata, row order, prompts and answers are unchanged. Use the existing verl reward functions directly. Each file has a separate Hub configuration because original train/evaluation schemas differ.
Use the full evaluation split and four independently sampled responses per prompt; report mean@4, not pass@4. No training responses or model weights are included. Exact normalized-chat train/evaluation overlap: 1 prompts. Original splits are preserved, including any disclosed overlap, to match existing teacher provenance. This is not a semantic contamination audit.
from datasets import load_dataset
train = load_dataset("caiyuchen/OPV-Science", "train", split="train")
validation = load_dataset("caiyuchen/OPV-Science", "validation", split="validation")The available artifacts differ from several manuscript descriptions. The accompanying repository records these differences instead of relabeling datasets or inventing results.
