datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hle_nofigurestrain_v6_deepseektrain_v6_qwen32bprefixestrain_v6_qwentmpmathtmpgenericmulti_biomed_french_prefixed_20250710train_v6deepseek-ai/DeepSeek-R1 difficulty labels are noisy as did not use simpleverify; others should be very clean
pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrm_0p2train_v6_filteredThis dataset is part of the paper Prefix Sliding for efficient test-time scaling. It contains training data for reinforcement learning to enable long-horizon reasoning.
Code is available at: https://github.com/Muennighoff/prefix-sliding
prm800k_onpolicy_multiturn_rtg_prefix0.2_roll4_maxrev100wjb_responses_prefixpile_trigram_prefixes
trigram_prefixes
See https://confirmlabs.org/posts/catalog.html for details.
id0: the first token in the trigram
id1: the second token in the trigram
id2: the most common token following (id0, id1) in The Pile
sum_count: the number of times that (id0, id1) appears in The Pile.
max_count: the number of times that id2 appears after (id0, id1) in The Pile.
frac_max: max_count / sum_count
dfhalftmpalfworld-expert-prefix-rollouts
ALFWorld Expert-Prefix Rollout Landscape
This dataset measures how a frozen language-model actor's probability of
solving an ALFWorld task changes after replaying different-length prefixes of a
successful expert trajectory.
The collection contains all 3,553 ALFWorld training tasks from the Agent-G2 SFT
data. Eight independent actor rollouts were sampled from the initial state for
every task. For the 2,307 low-signal tasks with at most one root success, eight
additional… See the full description on the dataset page: https://huggingface.co/datasets/YYYYYYibo/alfworld-expert-prefix-rollouts.pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrmtrain_v6_filtered_mathtask1319_country_by_barcode_prefix
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1319_country_by_barcode_prefix
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1319_country_by_barcode_prefix.pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrm_newsffop_1706381144_410msft_relabel_pythia6.9b_logprobs_prefix_chosentrain_v1text_and_concat_image_hf_version_epoch_1_with_prefix_with_exist_split_fixed_best_of_16_CoTprm800k_onpolicy_multiturn_rtgshape_prefix0.2_roll4_maxrev100prm800k_onpolicy_multiturn_cummrew_prefix0.1_roll4_maxrev100pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrm_post_inference_4p5mto5mtrain_v4pair_preference_model_dataset_add_prefix_to_win_rate0.1prm800k_onpolicy_multiturn_cumm_rew_prefix0.2_roll4_maxrev100pair_preference_model_dataset_add_prefix_to_win_rate0.1_rrm_post_inference_0to500k