datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nq-question-answeronlygsm8k_only_answerThe data is exactly like the original GSM8k (https://huggingface.co/datasets/gsm8k ), but with the label consisting of the correct answer(one number) only.
@misc{krishna2024gsmansweronly,
title={GSM8k (Answer only)},
author={Satyapriya Krishna},
year={2023},
url={skrishna/gsm8k_only_answer},
}
imaginative-perception-token-mvc-answeronly
Citation
Released with the paper Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models (arXiv:2606.03988):
@misc{bigverdi2026imaginativeperceptiontokensenhance,
title={Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models},
author={Mahtab Bigverdi and Linjie Li and Weikai Huang and Yiming Liu and Jaemin Cho and Jieyu Zhang and Tuhin Kundu and Chris Dangjoo Kim and Zelun Luo and Linda Shapiro and Ranjay… See the full description on the dataset page: https://huggingface.co/datasets/weikaih/imaginative-perception-token-mvc-answeronly.lvr-data-mvc_answeronlyspatial-imaginative-token-pt-answeronly
Spatial Imaginative Token — Path Tracing (Answer-only (label-only; also the answer-only half of mixed training))
Path Tracing (PT) training split for the Spatial Imaginative Token project (11204 samples).
Variant: Answer-only (label-only; also the answer-only half of mixed training).
Used by Spatial-Imaginative-Token:
download with python scripts/download_spatial_datasets.py --task pt.
2026-09-16-da-7-answer-only-mix
DA supervision answer; all 752 DA and 9284 identical replay rows
field
value
experiment
DA supervision answer; all 752 DA and 9284 identical replay rows
date_generated
2026-09-16
constitution
constitutions/claude_distilled_09_principles/constitution.md
source_repo
https://github.com/Matthew-Bozoukov/teaching_claude_why_replication.git @ 4648153af4b834b70bd2e5374f639aaad219c83c
models
Tokenizer Qwen/Qwen3.6-27B@6a9e13bd6fc8f0983b9b99948120bc37f49c13e9; replay… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-09-16-da-7-answer-only-mix.imaginative-perception-token-pet-answeronly
Citation
Released with the paper Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models (arXiv:2606.03988):
@misc{bigverdi2026imaginativeperceptiontokensenhance,
title={Imaginative Perception Tokens Enhance Spatial Reasoning in Multimodal Language Models},
author={Mahtab Bigverdi and Linjie Li and Weikai Huang and Yiming Liu and Jaemin Cho and Jieyu Zhang and Tuhin Kundu and Chris Dangjoo Kim and Zelun Luo and Linda Shapiro and Ranjay… See the full description on the dataset page: https://huggingface.co/datasets/weikaih/imaginative-perception-token-pet-answeronly.answer-only-gp-l-only-10k
Debunk the Myth of SFT Generalization Dataset
This dataset is associated with the paper "Debunk the Myth of SFT Generalization". The paper challenges the prevailing view that supervised fine-tuning (SFT) primarily memorizes training data and fails to generalize, in contrast to reinforcement learning (RL). It demonstrates that SFT can generalize as well as—or better than—RL when trained with appropriate data, achieved through prompt diversity and Chain-of-Thought (CoT) supervision on… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/answer-only-gp-l-only-10k.2026-09-01-answer-only-supervision-chunk-only-702
Answer-only supervision mixture, principle-scoped (Table2 9,284 + chunk-only 702)
field
value
experiment
Arm: train the 702 principle-scoped difficult-advice rows on their VISIBLE ANSWER ONLY -- the reasoning trace stays in the token stream as unsupervised context (no truncation, full forward pass) and simply earns no loss, while the 9,284 Table2 rows train exactly as in the control. The EXACT COMPLEMENT of the CoT-only arm on the same base: on every one of the 702… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-09-01-answer-only-supervision-chunk-only-702.genvf-filtered-answer-only-K4-summaries-nextN-prl-trainnq-question-answeronly_addy88_cleanedEklav-Reranker-AnswerOnly-Data
Eklav-Reranker-AnswerOnly-Data
Training data for the Eklav paper.
Task: passage reranking (BRIGHT / NevIR benchmarks)
Method: Answer-only (no reasoning of any kind -- the no-CoT floor)
Examples: 381,934 train / 3,857 held-out val
Format: ShareGPT (system + conversations: [{from, value}]), used for LoRA SFT via LLaMA-Factory.
Single-turn ShareGPT conversations. Each row: a query+passage relevance-judgment prompt (human turn) and a bare true/false judgment (gpt turn) -- no hint… See the full description on the dataset page: https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-AnswerOnly-Data.dpo_answer_only_with_gold_labels_kl_estimationdiverse-answer-only-gp-l-only-10k
General Points Dataset from Debunk the Myth of SFT Generalization
This dataset is part of the research presented in the paper Debunk the Myth of SFT Generalization. It contains data for the General Points decision-making benchmark, which is used to evaluate the generalization capabilities of Supervised Fine-Tuning (SFT) models against Reinforcement Learning (RL) baselines. The paper explores the impact of prompt diversity and Chain-of-Thought (CoT) supervision on SFT's ability to… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-gp-l-only-10k.genvf-filtered-answer-onlygenvf-filtered-answer-only-K4-summaries-nextNanswer-only-sokoban
Debunk the Myth of SFT Generalization
This dataset is part of the research presented in the paper Debunk the Myth of SFT Generalization.
The paper challenges the prevailing view that supervised fine-tuning (SFT) memorizes training data and fails to generalize, whereas reinforcement learning (RL) attains broader robustness. Through systematic evaluation on decision-making benchmarks like Sokoban and General Points, the authors demonstrate that introducing prompt diversity and… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/answer-only-sokoban.diverse-answer-only-sokoban
Dataset from "Debunk the Myth of SFT Generalization"
This dataset is associated with the research presented in the paper Debunk the Myth of SFT Generalization.
The paper challenges the conventional wisdom that supervised fine-tuning (SFT) primarily memorizes training data and struggles with generalization, contrasting it with reinforcement learning (RL)'s perceived robustness. Through systematic evaluation on decision-making benchmarks such as Sokoban and General Points, the… See the full description on the dataset page: https://huggingface.co/datasets/Xiaofeng77/diverse-answer-only-sokoban.dpo_answer_only_0.05_with_gold_labels_kl_estimationD-sft_gs__structure_types__answer_revision_only__masked_high_lr-sft-datatruthfulQA_first_500_best_answer_onlyTrivia_5_only_adversary_1086_gpt_wo_answer_stringnq-question-answeronly-With-Our-EmbeddingD-EVAL__standard_eval_v3__sft_gs__structure_types__answer_revision_only__masked_high_lr-eval_sft
D-EVAL__standard_eval_v3__sft_gs__structure_types__answer_revision_only__masked_high_lr-eval_sft
This evaluation dataset was created as part of the sft_gs__structure_types__answer_revision_only__masked_high_lr experiment using the SkillFactory experiment management system.
Experiment Tracking
🔗 View complete experiment details: Experiment Tracker Dataset
Evaluation Details
{"model": "TAUR-dev/M-sft_gs__structure_types__answer_revision_only__masked_high_lr-sft"… See the full description on the dataset page: https://huggingface.co/datasets/TAUR-dev/D-EVAL__standard_eval_v3__sft_gs__structure_types__answer_revision_only__masked_high_lr-eval_sft.audio_alpaca_train_answer_only