LeWM Push-T: My Experiment vs Paper (arXiv 2603.19312v3)

Legend: ✓ = matches · ≈ = close / likely same · ⚠️ = differs, likely affects results · — = not stated in paper

1. Training parameters

	Mine	Paper
Epochs	3	10	⚠️
Image size	112×112	224×224	⚠️
Encoder	ViT-tiny, patch 14, 12 layers, 3 heads, hidden 192, from scratch	ViT-tiny, patch 14, 12 layers, 3 heads, hidden 192, from scratch	✓
Embedding	[CLS] → MLP projector (hidden 2048) + BatchNorm, dim 192	[CLS] → 1-layer MLP + BatchNorm, dim 192	≈
Predictor	transformer, 6 layers, 16 heads, dropout 0.1, AdaLN action conditioning, ~10M params	transformer (“ViT-S”), 6 layers, 16 heads, 10% dropout, AdaLN, ~10M params	✓
History size	3	3	✓
Sub-trajectory	4 frames + 4 blocks of 5 actions	4 frames + 4 blocks of 5 actions	✓
Frameskip	5	5	✓
Batch size	128	128	✓
SIGReg λ	0.09	0.1 (λ ablation peaks near 0.09)	≈
SIGReg projections	1024	1024	✓
SIGReg knots	17	T nodes in [0.2, 4] (count not stated; insensitive)	—
Optimizer / LR / WD	AdamW / 5e-5 / 1e-3	—	—
Precision / grad clip	bf16 / 1.0	—	—
Dataset	`librakevin/lewm-pusht`	DINO-WM Push-T, 20,000 expert episodes	≈ (not verified)
Training seeds	1 (seed 3072)	3	⚠️

	Mine	Paper
Episodes	50	50 (same 50 for all models)	✓
Eval budget	50 steps	50 steps	✓
Goal offset	25 steps	25 steps	✓
CEM samples	300	300	✓
CEM iterations	30	30 (Push-T)	✓
CEM top-k	30	30	✓
CEM initial variance	1.0	1	✓
Planning horizon	5 (= 25 env steps)	5 (= 25 env steps)	✓
Receding horizon	5 (execute full plan)	5 (execute full plan)	✓
Eval image size	112	224	⚠️
Eval seed	42 (default in `config/eval/pusht.yaml`)	—	—

All 10 seeds together (same epoch-3 checkpoint, 500 episodes in total):

The ± is computed the same way as the paper’s. The sample std is ±5.5.