GlanceWAM: Sparse Test-Time Imagination for World-Action Models
Paper • 2608.23927 • Published • 1
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<chunk_shape: list<item: int64>, codecs: list<i (... 261 chars omitted)
child 0, item: struct<name: string, configuration: struct<chunk_shape: list<item: int64>, codecs: list<item: struct (... 249 chars omitted)
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>, codecs: list<item: struct<name: string, configuration: struct (... 212 chars omitted)
child 0, chunk_shape: list<item: int64>
child 0, item: int64
child 1, codecs: list<item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string (... 53 chars omitted)
child 0, item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string, clevel: i (... 41 chars omitted)
child 0, name: string
child 1, configuration: struct<endian: string, typesize: int64,
...
ormat: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
franka: struct<action: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, mi (... 312 chars omitted)
child 0, action: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, min: list<item: d (... 81 chars omitted)
child 0, mean: list<item: double>
child 0, item: double
child 1, std: list<item: double>
child 0, item: double
child 2, max: list<item: double>
child 0, item: double
child 3, min: list<item: double>
child 0, item: double
child 4, q01: list<item: double>
child 0, item: double
child 5, q99: list<item: double>
child 0, item: double
child 6, mask: list<item: bool>
child 0, item: bool
child 1, state: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, min: list<item: d (... 57 chars omitted)
child 0, mean: list<item: double>
child 0, item: double
child 1, std: list<item: double>
child 0, item: double
child 2, max: list<item: double>
child 0, item: double
child 3, min: list<item: double>
child 0, item: double
child 4, q01: list<item: double>
child 0, item: double
child 5, q99: list<item: double>
child 0, item: double
child 2, num_transitions: int64
child 3, num_trajectories: int64
to
{'franka': {'action': {'mean': List(Value('float64')), 'std': List(Value('float64')), 'max': List(Value('float64')), 'min': List(Value('float64')), 'q01': List(Value('float64')), 'q99': List(Value('float64')), 'mask': List(Value('bool'))}, 'state': {'mean': List(Value('float64')), 'std': List(Value('float64')), 'max': List(Value('float64')), 'min': List(Value('float64')), 'q01': List(Value('float64')), 'q99': List(Value('float64'))}, 'num_transitions': Value('int64'), 'num_trajectories': Value('int64')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
shape: list<item: int64>
child 0, item: int64
data_type: string
chunk_grid: struct<name: string, configuration: struct<chunk_shape: list<item: int64>>>
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>>
child 0, chunk_shape: list<item: int64>
child 0, item: int64
chunk_key_encoding: struct<name: string, configuration: struct<separator: string>>
child 0, name: string
child 1, configuration: struct<separator: string>
child 0, separator: string
fill_value: double
codecs: list<item: struct<name: string, configuration: struct<chunk_shape: list<item: int64>, codecs: list<i (... 261 chars omitted)
child 0, item: struct<name: string, configuration: struct<chunk_shape: list<item: int64>, codecs: list<item: struct (... 249 chars omitted)
child 0, name: string
child 1, configuration: struct<chunk_shape: list<item: int64>, codecs: list<item: struct<name: string, configuration: struct (... 212 chars omitted)
child 0, chunk_shape: list<item: int64>
child 0, item: int64
child 1, codecs: list<item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string (... 53 chars omitted)
child 0, item: struct<name: string, configuration: struct<endian: string, typesize: int64, cname: string, clevel: i (... 41 chars omitted)
child 0, name: string
child 1, configuration: struct<endian: string, typesize: int64,
...
ormat: int64
node_type: string
storage_transformers: list<item: null>
child 0, item: null
franka: struct<action: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, mi (... 312 chars omitted)
child 0, action: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, min: list<item: d (... 81 chars omitted)
child 0, mean: list<item: double>
child 0, item: double
child 1, std: list<item: double>
child 0, item: double
child 2, max: list<item: double>
child 0, item: double
child 3, min: list<item: double>
child 0, item: double
child 4, q01: list<item: double>
child 0, item: double
child 5, q99: list<item: double>
child 0, item: double
child 6, mask: list<item: bool>
child 0, item: bool
child 1, state: struct<mean: list<item: double>, std: list<item: double>, max: list<item: double>, min: list<item: d (... 57 chars omitted)
child 0, mean: list<item: double>
child 0, item: double
child 1, std: list<item: double>
child 0, item: double
child 2, max: list<item: double>
child 0, item: double
child 3, min: list<item: double>
child 0, item: double
child 4, q01: list<item: double>
child 0, item: double
child 5, q99: list<item: double>
child 0, item: double
child 2, num_transitions: int64
child 3, num_trajectories: int64
to
{'franka': {'action': {'mean': List(Value('float64')), 'std': List(Value('float64')), 'max': List(Value('float64')), 'min': List(Value('float64')), 'q01': List(Value('float64')), 'q99': List(Value('float64')), 'mask': List(Value('bool'))}, 'state': {'mean': List(Value('float64')), 'std': List(Value('float64')), 'max': List(Value('float64')), 'min': List(Value('float64')), 'q01': List(Value('float64')), 'q99': List(Value('float64'))}, 'num_transitions': Value('int64'), 'num_trajectories': Value('int64')}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
GlanceWAM reproduction bundle
Paper: GlanceWAM: Sparse Test-Time Imagination for World-Action Models
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:
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
# 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.
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