EasyWAM-Hidden-Wan22-LoRA-128

EasyWAM-Hidden conditions a separate Action DiT on intermediate features from a Wan2.2 Video DiT. This checkpoint was trained on LIBERO using rank-128 LoRA (r=128, alpha=128) with Wan2.2-TI2V-5B as the backbone.

The corresponding checkpoint was obtained by training with the EasyWAM codebase.

Results

Success rate (%) under the EasyWAM LIBERO evaluation protocol:

Model	Spatial	Object	Goal	Long	Avg.
Full-Parameter
EasyWAM-Unified	99.0	99.4	99.2	98.2	99.0
EasyWAM-MoT	97.8	98.4	97.6	95.6	97.4
EasyWAM-Hidden	99.4	100.0	97.0	97.8	98.6
LoRA (Rank 128)
EasyWAM-Unified	84.0	97.8	92.0	81.2	88.8
EasyWAM-MoT	96.8	98.8	94.4	90.4	95.1
🔥 EasyWAM-Hidden	96.8	99.4	92.6	86.8	93.9

Success rate (%) under the LIBERO-Plus evaluation protocol:

Model	Background	Camera	Language	Layout	Light	Noise	Robot	Avg.
EasyWAM-Unified	55.8	33.7	93.7	80.6	92.2	50.2	71.4	67.5
EasyWAM-MoT	52.8	20.6	80.4	65.2	85.1	51.5	49.7	56.8
EasyWAM-Hidden	56.8	49.2	95.3	81.0	90.4	58.2	77.4	72.4

Download

hf download OpenMOSS-Team/EasyWAM-Hidden-Wan22-LoRA-128 \
  easywam_hidden_wan22_lora_128.pt \
  --local-dir ./checkpoints

Evaluation

Prepare Wan2.2, LIBERO, and the matching dataset_stats.json as described in the EasyWAM LIBERO guide, then run:

python experiments/libero/run_libero_manager.py \
  task=libero_easywam_hidden_wan22_lora \
  ckpt=./checkpoints/easywam_hidden_wan22_lora_128.pt

EasyWAM creates the matching LoRA modules from the task config and merges them for evaluation when loading this checkpoint.

Checkpoint Details

• Architecture: EasyWAM-Hidden
• Backbone: Wan2.2-TI2V-5B
• Training: LoRA, rank 128, alpha 128
• Dataset: LIBERO, two cameras at 224 px
• Training steps: 20,000
• Action dimension: 7
• State dimension: 8
• Format: EasyWAM PyTorch checkpoint (.pt)

License and Citation

EasyWAM code is released under the MIT License. Use of this checkpoint is also subject to the terms of its base model and training data. See the EasyWAM repository for citation information.