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BooBooWu
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viscore

Tasks:
Robotics
Reinforcement Learning
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1K<n<10K
ArXiv:
Tags:
world-model
jepa
planning
model-predictive-control
representation-evaluation
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metadata 
license: mit
task_categories:
  - robotics
  - reinforcement-learning
tags:
  - world-model
  - jepa
  - planning
  - model-predictive-control
  - representation-evaluation
pretty_name: 'VIScore: datasets and reproduction bundle'
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files: data/*.zst

VIScore: datasets and reproduction bundle

arXiv Project Page GitHub Models

Contents:

  • 🗺️ MAZE: the held-out dataset, used to test whether the metric transfers to an unseen task family
  • 🧩 PushObj: six unseen object shapes plus the in-distribution control, for OOD planning
  • ♻️ Reproduction bundle: latents, covariance spectra, sobriety gaps and labels — every table recomputes on a CPU
  • 🔗 Base datasets: PushT / Reacher / Two-Room / Cube are LeWorldModel's and are linked, not re-hosted

Available Data

data/ — 1.15 GiB

zstd-compressed HDF5.

File Size Contents
maze2d_medium.h5.zst 600 MB 2000 episodes × 100 steps, 224² frames, converted from DINO-WM's point_maze release (D4RL maze2d-medium). State is (x, y, vx, vy); success is ‖agent − goal‖ ≤ 0.5.
pushobj_{L,Z,plus,I,small_tee,square,T}.h5.zst 61–121 MB each Six unseen shapes plus T, the in-distribution control.

PushObj is built by replaying the T-block expert action sequences from pusht_expert_train.h5 on each substituted shape, keeping episodes with at least one pusher–object contact (AdaJEPA App. A.2 protocol). Three properties affect absolute success rates on these files: the replays include block-static episodes; the success criterion ignores the object's rotational symmetry, which under-counts square, plus, Z and I; and the goal marker is rendered in the substituted shape. Comparisons between methods on the same file are unaffected.

bundle/ — 1.71 GiB

Path Contents
latents/<run>__ep<N>__<probe>.npz encoder output on the frozen probe, (F, 192)
spectra/<run>__ep<N>__<probe>.npz S (action-induced terminal displacement covariance) and E (teacher-forced residual covariance)
gaps/<run>__ep<N>__<probe>_gap.npz per-anchor sobriety gaps
probes/probe_<task>_nopixels.npz probe without the pixel array: action blocks, episode pointers, ground-truth state (~1 MB)
pool_manifest.csv per checkpoint: pool membership, success labels, seven metric values
pool_assignment.csv run → development / test fold
success_labels.csv 3104 planning evaluations: (checkpoint, task, goal offset, evaluation seed) → success rate
planning_arms.csv the epoch each reported table arm was taken at
heldout_method_cells.csv the held-out-method pool: metrics per checkpoint, frozen, plus which method it is
heldout_method_labels.csv its success rates, one row per (checkpoint, evaluation seed)

The held-out-method checkpoints from Qantara, RC-aux and INTACT are other groups' releases and are not re-hosted; reproduce/download_external.py fetches them from their own repositories. Their metrics are frozen in heldout_method_cells.csv because scoring them requires each source's own code checkout.

Base datasets

Task Repository File Compressed → decompressed
PushT quentinll/lewm-pusht pusht_expert_train.h5.zst 12.2 → 46 GB
Reacher quentinll/lewm-reacher reacher.tar.zst 22.1 → 99 GB
Two-Room quentinll/lewm-tworooms tworoom.tar.zst 3.2 → 13 GB
Cube quentinll/lewm-cube cube_single_expert.tar.zst 43.0 → 102 GB

Usage

Download with huggingface_hub

from huggingface_hub import hf_hub_download, snapshot_download

# one dataset
path = hf_hub_download(repo_id="BooBooWu/viscore", repo_type="dataset",
                       filename="data/maze2d_medium.h5.zst")

# the reproduction bundle
snapshot_download(repo_id="BooBooWu/viscore", repo_type="dataset",
                  allow_patterns=["bundle/*"])

Decompress with zstd -d --check data/*.zst.

Download with the repo helper

git clone https://github.com/HaiyuWu/viscore && cd viscore && pip install -e .

python reproduce/download.py --tier bundle   --dest $STABLEWM_HOME   # 1.7 GiB
python reproduce/download.py --tier datasets --dest $STABLEWM_HOME   # all six sources

Expected layout under $STABLEWM_HOME: pusht_expert_train.h5, dmc/reacher.h5, tworoom.h5, ogbench/cube_single_expert.h5, maze2d_medium.h5, pushobj_*.h5.

Reproduction

All three factors are linear algebra once the latents and spectra exist, so the bundle recomputes the paper's tables without a GPU:

python reproduce/tables.py                     # metric vs success, three pools
python reproduce/planning_tables.py --strict    # planning tables, gated against published values

Probes with pixels are not shipped; they are rebuilt deterministically with viscore probe (rng(0), 300 episodes, frameskip 5). Scores taken against a probe built with different settings are not comparable to published ones.

Citation

@article{wu2026viscore,
  title         = {VIScore: Diagnosing Planning-Relevant Quality in Latent World Models},
  author        = {Wu, Haiyu and Balestriero, Randall and Levine, Morgan},
  journal       = {arXiv preprint arXiv:2608.11174},
  year          = {2026},
  eprint        = {2608.11174},
  archivePrefix = {arXiv}
}

maze2d_medium.h5 is re-rendered from DINO-WM's point_maze release (D4RL maze2d-medium); PushObj derives from LeWorldModel's PushT expert data.

License

This project (code and data) is released under the MIT License.