datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
details_SenseLLM__ReflectionCoder-DS-33B
Dataset Card for Evaluation run of SenseLLM/ReflectionCoder-DS-33B
Dataset automatically created during the evaluation run of model SenseLLM/ReflectionCoder-DS-33B.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_SenseLLM__ReflectionCoder-DS-33B.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.UMM-Reflection-SFT-Data
UMM-Reflection SFT Data
The reflection-SFT data of
UMM-Reflection (Learning
Native Reflection in Unified Models). It trains
UMM-Reflection-BAGEL-SFT.
Research use only, non-commercial. The rows are derived from datasets
with different licenses, some of them non-commercial. Each row records its
source dataset and license in source_dataset and source_license, and
each row follows the terms of its source. See LICENSE.md.
Contents
Part
Rows
Shards
Size… See the full description on the dataset page: https://huggingface.co/datasets/YijiaFan/UMM-Reflection-SFT-Data.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.Reflection-Dataset-ShareGPT-v2
Simple "Reflection" method dataset inspired by mattshumer
This is the ShareGPT version. Find prompt and response pair dataset here
This dataset was synthetically generated using Glaive AI. There have been structure improvements and added more rows.
ReflectionEvoGithub Repo for ReflectEvo: https://github.com/bigai-nlco/ReflectEvo
Arxiv Paper for ReflectEvo: https://arxiv.org/abs/2505.16475
reflection_eval_prompt1test_reflection_eval_promptreflection_eval_prompt2ReflectionSeq-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.reflections__csqa_sft_train__p1details_terrycraddock__Reflection-Llama-3.1-8B
Dataset Card for Evaluation run of terrycraddock/Reflection-Llama-3.1-8B
Dataset automatically created during the evaluation run of model terrycraddock/Reflection-Llama-3.1-8B.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_terrycraddock__Reflection-Llama-3.1-8B.details_SenseLLM__ReflectionCoder-CL-34B
Dataset Card for Evaluation run of SenseLLM/ReflectionCoder-CL-34B
Dataset automatically created during the evaluation run of model SenseLLM/ReflectionCoder-CL-34B.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_SenseLLM__ReflectionCoder-CL-34B.llama3_8b_reflection29_8_25__countdown_3arg__sft_data_multiprompts_reflections9_8_25__letter_countdown_4o__sft_data_mp_reflectionReflection-Dataset-v2
Second version of a simple "Reflection" method dataset inspired by mattshumer
This is the prompt and response version. Find ShareGPT version here
This dataset was synthetically generated using Glaive AI. There have been structure improvements and added more rows.
o1_reflectionReflectionSeq-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.distilabel-reflection-tuning
Dataset Card for distilabel-reflection-tuning
This dataset has been created with distilabel.
The pipeline script was uploaded to easily reproduce the dataset:
reflection.py.
It can be run directly using the CLI:
distilabel pipeline run --script "https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning/raw/main/reflection.py"
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated… See the full description on the dataset page: https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning.9_8_25__countdown_4arg__sft_data_mp_reflection_ckpt_chunk_89_8_25__countdown_3arg__sft_data_GPT4o_multiprompts_gpt4o_reflections2026-08-04-qwen36-self-reflection-20-80-train
⚠️ SUPERSEDED — do not train from this bundle
Built 2026-08-04 under the old rendering policy, where Qwen3.6 emitted a <think> block on the
final assistant turn only. The repository has since moved to preserve-thinking rendering, in
which every assistant turn carries a think block (real trace, or the empty marker). Both files here
are stale as a result:
mixture.jsonl — rendered under the old policy, so it trains different strings than the current
pipeline produces. It also… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-04-qwen36-self-reflection-20-80-train.9_8_25__letter_countdown_4o__sft_data_mp_reflection_ckpt_chunk_5reflections__gsm8k_sft_train__backup_2f525e6SenseLLM__ReflectionCoder-CL-34B-details
Dataset Card for Evaluation run of SenseLLM/ReflectionCoder-CL-34B
Dataset automatically created during the evaluation run of model SenseLLM/ReflectionCoder-CL-34B
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/SenseLLM__ReflectionCoder-CL-34B-details.reflection_n16multitask_intermediate_ac4_v2_reflections5_formats-C_fullreflection9_8_25__countdown_4arg__sft_data_mp_reflection