AI Dataset2026
Pairs natural-language instructions with executable setup artifacts and Python reward functions to create verifiable computer-use agent tasks. Provides a Parquet task table for fast filtering plus a compressed archive of runnable task bundles; several web task endpoints are placeholders that require a local CUA-Gym-Hub deployment.
AI Dataset2026
25,000 chat-formatted synthetic SFT examples distilled to emulate the reasoning style and agentic behavior of Anthropic's Claude Mythos, focused on cybersecurity, advanced coding, mathematical reasoning, and long-horizon agent tasks. Includes metadata for targeted curriculum fine-tuning and is Apache-2.0 licensed.
AI Dataset2026
Transfers pretrained latent diffusion priors into pixel space to train pixel-space diffusion models using only synthetic images from LDMs. Trains shallow pixel layers while freezing most LDM internals, reducing data and compute needs and enabling native 4K generation without a VAE.
AI Dataset2026
Provides 10,000 articulated 3D objects in URDF for robotics and embodied-AI research. Generated by the Articraft agent and released under CC-BY-4.0, the dataset targets simulation, manipulation, kinematics evaluation, and training of embodied agents.
AI Dataset2026
Provides 40 public Kubernetes incident scenarios (SRE subset) with ground-truth root-cause entities and offline cluster snapshots in JSONL format; designed to evaluate agentic root-cause diagnosis on alerts, events, traces and topology.
AI Dataset2026
Collection of 1,000 AI-generated dreamcore aesthetic images (2K JPEGs, numbered 001–1000) intended for creative prototyping and visual research. Images were produced with GPT Image 2 and released under an MIT license.
AI Dataset2026
Provides a large-scale ASR corpus organized by normalized acoustic subsets for robustness training and evaluation. About 645,925 examples across 54 acoustic conditions (noise, echo, far-field, recording distortions) with many distortion/dropout/noise Parquet splits. Distributed as split Parquet files; license not specified on the dataset page.
Measures how well LLMs and agent-driven workflows prepare supervised training data end-to-end by jointly benchmarking data construction and data-quality evaluation across six domains, using a downstream-grounded protocol and new metrics.
AI Dataset2026
Dataset of 5,000 reconstructed chain-of-thought samples produced by trace‑inversion from Claude‑opus‑4.7 summaries — packaged for SFT/DPO fine‑tuning. Key features: reconstructed CoT traces, multilingual prompts, gzip .jsonl format. Best used for reasoning distillation and model-level supervision; synthetic traces may need extra verification.
AI Dataset2026
Provides 9,000 reconstructed chain-of-thought (CoT) SFT examples produced by trace inversion from Claude Opus 4.6 outputs for fine-tuning reasoning-capable LLMs. Multilingual, packaged as .jsonl.gz and SFT/DPO-ready; verify numeric/code cases before training.
AI Dataset2026
RL training dataset for long-context language-model fine-tuning with ~23K samples and nine reward types, provided in Parquet with bilingual ground-truth and reward metadata for direct RL/bench evaluation.
AI Dataset2026
Supervised fine-tuning dataset of instruction-style examples in English and Chinese covering generation, QA, reasoning, math and code — targeted for SFT of 10–100B-parameter LLMs. Associated with arXiv:2602.09003; first published May 21, 2026.