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fastwam-dexjoco-uncond-3cam224-100k (EgoDex init, v1)
Trained with FastWAM (arunos728/fastwam_test) on the dexjoco 11-task corpus,
4 GPU x batch 16 = global batch 64, 100k steps, lr 1e-4 cosine.
This run did not start from scratch. It was initialised from an EgoDex
human-hands checkpoint (30k steps, single base camera at 192x320, 26 task
dirs / 44,507 episodes / 12.2M frames).
Three tensor groups were dropped from that checkpoint before loading, because they are embodiment-specific -- EgoDex is human hands and dexjoco is a robot, so index i means a different joint in each:
| dropped | shape |
|---|---|
mixtures.action.action_encoder.{weight,bias} |
(1024, 44) |
mixtures.action.head.{weight,bias} |
(44, 1024) |
proprio_encoder.{weight,bias} |
(4096, 44) |
1645 of 1649 mot tensors carried over -- the video/action expert transformer
stack, which is embodiment-agnostic. The dropped layers were reinitialised.
proprio_encoder had to go regardless: it loads with strict=True and EgoDex is
44-wide against dexjoco's 46.
Weights only: checkpoints/state/ (DeepSpeed ZeRO-1 optimizer state) is not
published, so this is for evaluation rather than resuming.
v1 takes its init from the EgoDex run trained with a cosine lr decay. Its sibling -egodexinit-v2 is identical except that its init came from an EgoDex run held at a constant lr after warmup, so the pair isolates the effect of the pretraining schedule.
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