datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lsat-reasoning-traces
LSAT Reasoning Traces
Model chain-of-thought reasoning traces collected while evaluating models on the
ooakdata/lsat-reasoning
LSAT benchmark. One row per (model, question) over the corrected validation
split (1493 questions). Join back to the benchmark on id for the question text,
answer choices, scenario, and explanation.
These are model evaluation traces, not agent-session logs.
Includes every evaluated configuration: self-hosted models in both direct
(single-letter) and cot… See the full description on the dataset page: https://huggingface.co/datasets/ooakdata/lsat-reasoning-traces.MiMo-2.5-Pro-Reasoning-Traces-Hard
MiMo-2.5-Pro-Reasoning-Traces-Hard
A large-scale reasoning dataset of 8,706 expert-level prompts with full reasoning traces across 44 academic and technical topics, generated using the MiMo-v2.5-Pro model. Each entry contains the step-by-step reasoning chain alongside the final completion, designed for training and evaluating advanced reasoning capabilities in language models.
Dataset Statistics
Metric
Value
Total entries
8,706
Unique topics
44… See the full description on the dataset page: https://huggingface.co/datasets/Skyhigh-2203/MiMo-2.5-Pro-Reasoning-Traces-Hard.reasoning-traces-multilingual
OpenEuroLLM Multilingual Mathematical Reasoning Traces — Two-Stage Pilot
Release status: private v0.2-pilot staging dataset. All published rows passed the
deterministic translation gates described below. This pilot has not yet completed a systematic
native-speaker audit or independent downstream-solver verification and is not a final production
training release.
This dataset contains 3,425 accepted translations sampled from
100 mathematical reasoning traces into 37 non-English… See the full description on the dataset page: https://huggingface.co/datasets/openeurollm/reasoning-traces-multilingual.minimax-m3-reasoning-traces
MiniMax-M3 Reasoning-Traces v1
A multi-format supervised fine-tuning (SFT) corpus of reasoning traces covering six
domains: math, coding, reasoning, agentic, tool use, and instruction following.
Each trace is a structured step-by-step solution followed by a final answer, with
explicit intermediate steps and verification.
Files
File
Format
Traces
Size
data/alpaca.jsonl
Alpaca (instruction, input, output)
1059
3.3 MB
data/chat.jsonl
Multi-turn chat… See the full description on the dataset page: https://huggingface.co/datasets/Kenshiii/minimax-m3-reasoning-traces.deepseek-v4-pro-pi-reasoning-sample-traces
DeepSeek V4 Pro Pi Reasoning Sample Traces
This dataset contains a compact sample of successful DeepSeek V4 Pro teacher trajectories for Pi-style reasoning and tool-use workflows.
It includes selected pass-only traces from these task providers:
abcbench
aider
autocodebench
bfcl
swebench
swtbench
termigen
Format
Each row contains:
id: stable sample id
segments: Qwen-style template-free supervised segments
label=false segments are context only
label=true segments… See the full description on the dataset page: https://huggingface.co/datasets/bytkim/deepseek-v4-pro-pi-reasoning-sample-traces.Reasoning_Traces
CogniSQL Reasoning Traces
Dataset Summary
The CogniSQL Reasoning Traces dataset is a curated collection of 5,024 reasoning traces that support interpretable and efficient Text-to-SQL generation research. Each example includes natural language questions, step-by-step reasoning processes, executable SQL queries, and database contexts of varying lengths. This dataset is designed to improve model transparency and enable research into how language models approach complex SQL… See the full description on the dataset page: https://huggingface.co/datasets/CogniSQL/Reasoning_Traces.High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning
PyReason-7k: Advanced Python Chain-of-Thought Dataset
Dataset Description
This dataset contains 7,000+ high-quality Python programming examples designed for LLM fine-tuning.
Each entry includes a detailed thought_process (Chain-of-Thought) to teach models logical reasoning before coding.
Key Features:
Chain-of-Thought: Step-by-step reasoning traces.
Error Handling: Solutions include try-except blocks and logging.
Diverse Tasks: Algorithms, API handling, Data Structures.… See the full description on the dataset page: https://huggingface.co/datasets/xTayyub/High-Quality-Synthetic-Python-Dataset-with-Reasoning-Traces-Chain-of-Thought-for-LLM-Fine-Tuning.dynamics-reasoning-traces-sample
DYNAMICS-8 Behavioural Reasoning Traces
Personality-conditioned chain-of-thought reasoning data for LLM alignment and persona fine-tuning.
What This Dataset Contains
Each record is a first-person behavioural response from a synthetic persona with a validated 8-dimension personality profile (DYNAMICS-8), accompanied by a structured reasoning trace showing which personality dimensions drove the decision.
This is not survey data. It is not statistical synthetic data. Each… See the full description on the dataset page: https://huggingface.co/datasets/Kronaxis/dynamics-reasoning-traces-sample.hermes-agent-reasoning-traces
Hermes Agent Reasoning Traces
Structured fine-tuning dataset extracted from Hermes Agent execution logs and skill files.
Examples: 688
Skill examples: 688
Session examples: 0
Source: Hermes Agent (Nous Research)
Generated: 2026-08-16
Format
Each line is a JSON object with:
instruction: The user request or skill creation prompt
response: The agent's response or skill body
source: Origin (skill file or session ID)
category: Type (skill_creation or conversation)… See the full description on the dataset page: https://huggingface.co/datasets/zombierotten/hermes-agent-reasoning-traces.JOSIE-Zero-8B-Reasoning-Traces-N67
JOSIE-Zero-Reasoning-Traces-N86
Reasoning traces generated by the JOSIE-ZERO-8B model.
JOSIE-ZERO-8B is a custom reasoning model trained using the GRPO (Group Relative Policy Optimization) training pipeline implemented in the MLX-LM-LoRA framework. The model was optimized with custom reward functions designed to encourage explicit reasoning, chain-of-thought style problem solving, self-correction, and structured analytical behavior.
This dataset contains high-quality reasoning… See the full description on the dataset page: https://huggingface.co/datasets/mlx-community/JOSIE-Zero-8B-Reasoning-Traces-N67.domain-agnostic-reasoning-traces-balanced-top50-v1
BOTCOIN Balanced Top-50 Reasoning Traces
This public dataset contains enriched BOTCOIN reasoning-trace attempts selected
from canonical dataset/v2 research-ready objects.
Selection policy:
Source only attempts/research-ready objects.
Rank each domain by trace_quality.reasoning_trace_quality_score.
Keep each domain's top 50 percent.
Equalize domains to the smallest top-half count.
The rows are self-contained and intentionally rich: prompt/messages… See the full description on the dataset page: https://huggingface.co/datasets/botcoinmoney/domain-agnostic-reasoning-traces-balanced-top50-v1.medqa-reasoning-traces
MedQA with LLM Reasoning Traces (kimi-k3)
A derived dataset pairing every MedQA USMLE question with a reasoning trace
produced by a large language model, the model's predicted answer, and a
correctness flag scored against the gold label.
Source questions: bigbio/med_qa, subset med_qa_en_4options_source (English, 4-option USMLE variant), splits train/validation/test
Records: 12,723 (one per question × model)
Overall accuracy: 94.4% (12,011/12,723)
Why this dataset?… See the full description on the dataset page: https://huggingface.co/datasets/birgermoell/medqa-reasoning-traces.reasoning-traces-multilingual
OpenEuroLLM Multilingual Mathematical Reasoning Traces — TranslateGemma Pilot
Release status: private v0.1-pilot staging dataset. The rows passed the automated
translation gates described below, but this pilot has not yet completed a systematic
native-speaker audit or independent downstream-solver verification. It should not yet be treated
as a final production training release.
This dataset contains 2,930 accepted translations of mathematical reasoning traces into 37… See the full description on the dataset page: https://huggingface.co/datasets/birgermoell/reasoning-traces-multilingual.axiom-reasoning-traces
AXIOM Compact Distilled Reasoning Traces
A dataset of compressed, step-segmented math reasoning traces with per-step process reward labels
across five quality axes (logic, commonsense, consistency, efficiency, confidence). Created as part
of the AXIOM framework for training cross-domain Process Reward Models (XD-PRM) and
GRPO-tuned Small Language Models.
Repositories & Resources
Resource
Link
GitHub (AXIOM)
https://github.com/anishgrover72-droid/axiom… See the full description on the dataset page: https://huggingface.co/datasets/prabindersinghh/axiom-reasoning-traces.