dynamic
Datasets
All datasets matching “dynamic”dojo_market_dynamicsHydroGym-environmentsdynamics-toy
Toy v2.1: independent families and crossed factors
This is the replacement Toy v2.1 release requested by the dataset owner. It supersedes the initial v2.1 extension at commit 2023f1d2254394f469877a4e2af3e6b78fa415a3. The unpublished development name was v2.2; there is no separate active v2.2 release. Pin the new commit SHA for reproducibility, because the v2.1 tag selects this replacement. Preregistered opaque v22_* family/point IDs retain their identity and are not version… See the full description on the dataset page: https://huggingface.co/datasets/seooyxx/dynamics-toy.rl-dynamics-gradient-sketches-toy-adam5e5
Gradient sketches of a toy RL run (Adam, lr 5e-5, rep 3)
Random-projection sketches of per-rollout policy gradients from a small GRPO run in Apollo Research's RL-dynamics
project (base model Qwen/Qwen3.5-4B with LoRA adapters, 129,859,584 trainable parameters, 20 training steps,
1,024 rollouts per step). Every gradient was projected with the same subsampled randomised Hadamard transform (SRHT)
to k = 262,144 coordinates, so inner products between sketches approximate inner… See the full description on the dataset page: https://huggingface.co/datasets/apollo-research/rl-dynamics-gradient-sketches-toy-adam5e5.STUZero-Atari-Dynamics
STUZero Atari Dynamics Dataset
Offline dynamics training datasets collected from trained EfficientZero V2 (EZv2) benchmark models on Atari games. Each game's data is stored in a subfolder named {game}_{steps} indicating the game and the number of training steps of the source checkpoint. While all models were trained for 120K steps, best results in some games were attained at earlier checkpoints. The model with best eval scores was used to curate data for each game.… See the full description on the dataset page: https://huggingface.co/datasets/Shivamkak/STUZero-Atari-Dynamics.humanoid-robots-training-dataset
Dynamic Intelligence — Humanoid Robot Training Dataset
A first-person (egocentric) video dataset of human hand manipulation, designed for training humanoid robot policies via imitation learning. Each episode captures a person performing an everyday household task — folding clothes, moving dishes, opening doors — filmed from a head-mounted iPhone using its built-in LiDAR and depth sensors.
The dataset pairs each video with frame-level 3D hand tracking and camera pose data, giving… See the full description on the dataset page: https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset.