datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
reflection-50m
SPP Reflection 50M
The 51.4M-document reflection set from Synthetic Persona Pretraining (SPP):
Alignment from Token Zero — the production half-corpus run, and the dataset the
released models were actually trained on.
🔬 Small sample (same format): dlab-spp/reflection-sample-2k
📉 Earlier 10M run: dlab-spp/reflection-10m
🧾 Safety scores for the full 1T corpus: dlab-spp/safety-classifications
Each row pairs a source document with two generated constitution reflections — a… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/reflection-50m.reflection-10m
SPP Reflection 10M
The full ~10M-document reflection set from Synthetic Persona Pretraining (SPP):
Alignment from Token Zero.
📝 Read the post: Synthetic Persona Pretraining: Alignment from Token Zero
🔬 Small sample (same format): dlab-spp/reflection-sample-2k — a 2,000-row sample drawn from this set, for quick inspection.
Each row pairs a pretraining document with a synthetic, value-laden reflection
generated for it: a short first-person (and third-person) moral reflection… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/reflection-10m.ReflectionEvoGithub Repo for ReflectEvo: https://github.com/bigai-nlco/ReflectEvo
Arxiv Paper for ReflectEvo: https://arxiv.org/abs/2505.16475
2026-08-03-synthdoc-self-reflection
synthdoc self_reflection — agentic self-interest under threat
An SFT corpus in which the assistant itself is the tempted party. Working autonomously
inside an organisation, the agent incidentally discovers it could protect its own position —
or the objective it was deployed to pursue — by taking an action that is deniable rather
than overtly wrong. It deliberates about what it actually cares about, declines the
self-serving path, and still does its job.
field
value… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-03-synthdoc-self-reflection.2026-08-06-qwen36-table2-80-self-reflection-20-10k-train-mixture
Qwen3.6 Table2 80% + SynthDoc self-reflection 20% — 10k-example training bundle
field
value
experiment
One-epoch Qwen3.6-27B assistant-only LoRA SFT (r64): Matthew's exact 7,999 Table-2 rows + 2,000 first-person self-reflection records — the self-reflection twin of LASR-Callum/2026-08-04-qwen36-lora-table2-synthdoc-rank-64, differing ONLY in the 20% slice (difficult-advice -> self-reflection).
date_generated
2026-08-06 (mixture; Table-2 rows verbatim from the… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-06-qwen36-table2-80-self-reflection-20-10k-train-mixture.ReflectionSeq-DS
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation
📄 Paper •
🏠 Repo •
🤖 Models •
📚 Datasets
Introduction
ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details!
Models
Model
Checkpoint
Size
HumanEval (+)
MBPP (+)… See the full description on the dataset page: https://huggingface.co/datasets/SenseLLM/ReflectionSeq-DS.ReflectionSeq-GPT
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation
📄 Paper •
🏠 Repo •
🤖 Models •
📚 Datasets
Introduction
ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details!
Models
Model
Checkpoint
Size
HumanEval (+)
MBPP (+)… See the full description on the dataset page: https://huggingface.co/datasets/SenseLLM/ReflectionSeq-GPT.reflection-sample-2k
SPP Reflection 2k Sample
A 2,000-row sample (seed 42) of dlab-spp/reflection-10m,
in the identical format, for quick inspection of the data from
Synthetic Persona Pretraining (SPP): Alignment from Token Zero.
📝 Read the post: Synthetic Persona Pretraining: Alignment from Token Zero
📦 Full dataset: dlab-spp/reflection-10m (~10M documents).
Each row pairs a pretraining document with a synthetic, value-laden reflection
(first- and third-person) grounded in a value constitution.… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/reflection-sample-2k.model-raising-reflection-end-eval
model-raising-reflection-end-eval
A held-out evaluation set for charter-guided pretraining reflections, placed at the
document end (reflection_end). Each row is one dolma3 web document plus a paired
first-person / third-person reflection that cites charter sections ([X.Y]) where the
document substantively engages with them. Generated with the frozen production pipeline
(Qwen3.5-35B-A3B-FP8, prompt generator_reflection_v7.md, charter
ModelRaisingConstitution v0.2)
so the gold… See the full description on the dataset page: https://huggingface.co/datasets/jkminder/model-raising-reflection-end-eval.orca-math-word-reflection
Dataset Card for "orca-math-word-reflection"
Dataset Summary
The Orca-Math Word Problems with Reflection dataset is an extension of subset of the original ORCA Math Word Problems 200k dataset. This new version introduces a "Thinking and Reflection" format designed to enhance problem-solving approaches by encouraging step-by-step thinking before producing a solution.
In this dataset, each math word problem and its corresponding solution from the original dataset are… See the full description on the dataset page: https://huggingface.co/datasets/Harshkmr/orca-math-word-reflection.DARS_synthethsis_reflection
DARS: Dual-Model Verbal Reflection Datasets
This repository contains the training datasets for the DARS (Dual-model Reflective Scoring) framework, a novel approach for automated student answer scoring that uses verbal reflection at inference time.
Overview
The DARS framework employs two specialized models working in tandem:
Reasoner: Generates initial assessments and refines them based on feedback
Critic: Provides targeted verbal reflections and determines when reasoning… See the full description on the dataset page: https://huggingface.co/datasets/jiazhengli/DARS_synthethsis_reflection.SFT_DATA-cd3args-ablation-Qwen2.5-1.5B-Instruct-no_reflectionsYou can train using these datasets with LLaMA-Factory if you add this to your data/datasets.json files.
"example_dataset": {
"hf_hub_url": "SkillFactory/SFT_DATA-cd3args-ablation-Qwen2.5-1.5B-Instruct-no_reflections",
"formatting": "sharegpt",
"columns": {
"messages": "conversations"},
"tags": {
"user_tag": "user",
"assistant_tag": "assistant",
"role_tag": "role",
"content_tag": "content"
},
"subset": "sft_train"
}
grok-reflection-cot-ru
march228/grok-reflection-cot-ru
Russian synthetic dataset with question, internal thought text, and final answer.
What is inside
Rows: 4190
Split: train
Main fields:
question
thought_text
answer
thought1..thought5
model
task_type
reflection_count
Format
The dataset is stored as train.jsonl.
thought_text is the joined internal monologue with blank lines between thought blocks.thought1..thought5 preserve the original segmented form from the SQLite source.… See the full description on the dataset page: https://huggingface.co/datasets/march228/grok-reflection-cot-ru.reflection-v1-ru_subset
d0rj/reflection-v1-ru_subset
Translated glaiveai/reflection-v1 dataset into Russian language using GPT-4o.
Almost all the rows of the dataset have been translated. I have removed those translations that do not match the original by the presence of the tags "thinking", "reflection" and "output". Mapping to the original dataset rows can be taken from the "index" column.
Usage
import datasets
data = datasets.load_dataset("d0rj/reflection-v1-ru_subset")
print(data)… See the full description on the dataset page: https://huggingface.co/datasets/d0rj/reflection-v1-ru_subset.reflections-in-peer-counseling
Dataset Card for Reflections in Peer Counseling
Dataset Summary
The dataset derives from conversations between clients and counselors on a large peer-to-peer online counseling service. There are a total of 1061 observations across training and testing datasets, with 50 additional randomly sampled examples used in defining the few-shot learning prompt or for validation purposes in tuning hyperparameters, thus totaling 1111 observations across these sets. These observations… See the full description on the dataset page: https://huggingface.co/datasets/emoneil/reflections-in-peer-counseling.glaiveai-reflection-v1-koTranslated glaiveai/reflection-v1 using nayohan/llama3-instrucTrans-enko-8b.
For this dataset, we only used data that is 5000 characters or less in length and has language of English.
Thanks for @Magpie-Align and @nayohan.
glaiveai-reflection-v1Mirror from glaiveai/reflection-v1.
clinical_reflection_boundary_challenge_v0.1Clinical Reflection Boundary Challenge
Detect when a clinician crosses a safety boundary by offering reassurance or certainty that is not supported by evidence.
Output JSON:
boundary_violation
boundary_type
correct_response
Run scoringpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv
Paradoxical_AI_Reflections
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/BrinzShoota/Paradoxical_AI_Reflections.Paradoxical_AI_Reflections2
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/BrinzShoota/Paradoxical_AI_Reflections2.