File size: 3,123 Bytes
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pretty_name: GlanceWAM reproduction bundle
license: other
task_categories:
- robotics
tags:
- robotics
- vision-language-action
- world-model
- libero
- robocasa
- arxiv:2608.23927
---
# GlanceWAM reproduction bundle
**Paper**: [GlanceWAM: Sparse Test-Time Imagination for World-Action Models](https://huggingface.co/papers/2608.23927)
Everything needed to reproduce the GlanceWAM results on LIBERO and RoboCasa kitchen.
```
datasets/ LeRobot v3 datasets, UMT5 text caches included (glancewam_cache/)
checkpoints/ released checkpoints, one directory per run
```
Point the code at this directory:
```bash
export DATA_ROOT=/data/glancewam_release/datasets # training + precompute
ln -s /data/glancewam_release/checkpoints <repo>/results/Checkpoints
```
## Checkpoints
| Directory | Benchmark | Reported |
|---|---|---|
| `glancewam_robocasa_kitchen` | RoboCasa kitchen, 24 tasks x 50 episodes | **0.721** |
| `glancewam_libero` | LIBERO 4-in-1, 4 suites x 500 episodes | **0.989** |
Kitchen run-to-run noise is ~0.02 (the environment is paired but the policy is unseeded), so
treat anything within ~+/-0.02 as a match. LIBERO is saturated; differences under ~0.005 are noise.
## Datasets
| Directory | Used by |
|---|---|
| `libero_{spatial,object,goal,10}_no_noops_1.0.0_lerobot` | LIBERO (mixture `libero_all`) |
| `robocasa_cosmos_kitchen/` | RoboCasa kitchen, 24 per-task datasets (mixture `robocasa_kitchen_all`) |
Each dataset already carries its precomputed UMT5 text cache under
`<dataset>/glancewam_cache/t5/Skywork_SkyReels-V2-DF-1.3B-540P-Diffusers_L512`, so training can run
with `RESIDENT_TEXT_TABLE=True` (the default) without running the precompute step first.
The dataset trees are hard-linked from `/data/lerobot_v3`, so they cost no extra disk on this
machine; copying the directory elsewhere produces independent full copies.
## Code
<https://github.com/linhanwang/GlanceWAM>
```bash
# everything (21 GB)
hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle
# or just one benchmark
hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle \
--include "checkpoints/glancewam_libero/*" "datasets/libero_*" # 5.0 GB
hf download LinhanWang/GlanceWAM --repo-type dataset --local-dir ./glancewam_bundle \
--include "checkpoints/glancewam_robocasa_kitchen/*" \
"datasets/robocasa_cosmos_kitchen/*" # 16.2 GB
# then, from the code checkout
mkdir -p results
ln -s /abs/path/to/glancewam_bundle/datasets results/Datasets
ln -s /abs/path/to/glancewam_bundle/checkpoints results/Checkpoints
```
## Attribution
The datasets here are derived from third-party releases and remain subject to their original
terms: **LIBERO** (Lifelong Robot Learning, MIT) and the **RoboCasa** kitchen task suite as
distributed by NVIDIA's cosmos-policy release (RoboCasa Team, MIT). The checkpoints are fine-tuned
from **SkyReels-V2-DF-1.3B-540P** (Skywork) and inherit that model's terms. The GlanceWAM code
itself is MIT-licensed; see the code repository.
|