datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
financial-world-model
Twelve Data World Model Dataset
A multi-modal financial time-series dataset built from Twelve Data
market data. Each timeframe is published in three parallel views:
bars_* — OHLCV bars enriched with causal technical indicators and macro
context, in Parquet.
text_* — instruction-tuning prompts/labels derived from the bars, in
JSONL.
trajectories_* — fixed-length rolling windows of state vectors plus
next-state pairs, suitable for world-model / sequence-model training, in… See the full description on the dataset page: https://huggingface.co/datasets/twelvedata/financial-world-model.world_model_corpus
Dataset Card for World Model Corpus
Paper | GitHub
The world model corpus contains a set of generated trajectories that are shaped for text-based world modeling task as used by the paper: "Masked Diffusion Language Models are Strong and
Steerable Text-Based World Models for Agentic RL". The dataset contains trajectories from nine distinct environments: Tau2Bench, SWE-Smith, DeepresearchQA, Openresearcher, Gorilla/BFCLv4, Webshop, Toolathlon, Pandora and Coderforge.… See the full description on the dataset page: https://huggingface.co/datasets/PatronusAI/world_model_corpus.world-model-pi-sft-with-cot
world-model-pi-sft-with-cot
SFT-ready data for training a world model of the TeichAI pi coding-agent harness. Each
world-model assistant target carries a teacher-distilled <think>{rationale}</think> block
explaining why the next environment turn follows from prior context, followed by the original
[developer] / [tool:*] environment block.
Stage 2 of a 2-stage pipeline. Stage 1 dataset (with <COT_PLACEHOLDER> slots) is at
kfallah/world-model-pi-sft-formatted.
Schema… See the full description on the dataset page: https://huggingface.co/datasets/kfallah/world-model-pi-sft-with-cot.subjective-world-model
Subjective World Model — Preview Dataset
by Atypica
Preview release. All records are illustrative archetypes constructed from real interview methodology — not records of specific individuals.
Introduction
This dataset trains AI agents to simulate how specific people think and make decisions — not statistical averages or fictional archetypes, but individuals with their own logic, habits, and contradictions.
Two record types:
persona — one individual: an AI-conducted… See the full description on the dataset page: https://huggingface.co/datasets/atypica/subjective-world-model.world-model-pi-sft-formatted
world-model-pi-sft-formatted
Role-flipped formatted SFT data for training a world model (environment simulator) over the
TeichAI pi coding-agent harness. Source traces: TeichAI/DeepSeek-v4-Pro-Agent (4006 sessions).
This dataset is stage 1 of a two-stage pipeline: stage 2 (world-model-pi-sft-with-cot)
injects synthetic <think> rationales into every assistant target via a reasoning-model
teacher; this stage 1 dataset has only the placeholder <COT_PLACEHOLDER> token that stage 2… See the full description on the dataset page: https://huggingface.co/datasets/kfallah/world-model-pi-sft-formatted.babyai-world-model-sft
BabyAI World Model SFT Dataset
Training data for supervised fine-tuning (SFT) of a world model for the BabyAI grid-world environment. The world model learns to predict the next observation and available actions given the current state and the agent's action.
Dataset Description
Each example is a single environment transition in chat format (system/user/assistant):
System: Detailed simulator prompt with environment rules and task completion logic
User: Goal, current… See the full description on the dataset page: https://huggingface.co/datasets/GGOSinon/babyai-world-model-sft.WorldModelForMazeWithX
WorldModelForMazeWithX
Maze pathfinding sequences for training/probing sequence models (Transformer, Mamba, GRU, Gated-DeltaNet, ...). Task C1: relative-turn navigation on a fixed 10×10 directed grid. Includes a special x terminator marking wall-hit (illegal) paths, used to study a model's ability to recognize its own errors.
Maze
10×10 grid, 100 nodes (0–99). Directed edges (down/right, both directions added), edge probability 0.6.
Graph:… See the full description on the dataset page: https://huggingface.co/datasets/Kalso42/WorldModelForMazeWithX.