pi05_mvtoken_22_27_04

pi05_mvtoken_22_27_04

pi0.5 SFT checkpoints fine-tuned on the MVTOKEN_22_27_04 real-robot dataset.

Checkpoints

Two checkpoints from the same run, laid out as in the training output directory:

global_step_2000/actor/model_state_dict/full_weights.pt   # train loss ~0.014,  93 passes
global_step_4000/actor/model_state_dict/full_weights.pt   # train loss ~0.008, 177 passes
norm_stats.json                                           # shared by both

Evaluate both. The dataset is small (5,070 frames), so by step 4000 every frame has been seen ~177 times. For comparison, the SweepIntoDustpan-v1_Real run reached the same loss level after only ~15 passes — a lower training loss here does not imply a better policy.

norm_stats.json is the same file as in aaroncaozj/pi05_norm_stats_collection → MVTOKEN_22_27_04/.

FSDP distributed-checkpoint shards (dcp_checkpoint/*.distcp, ~22 GB each) are not included; they only serve to resume training and are not needed for inference.

Data

Training

Trained with RLinf (examples/sft/config/mvtoken_sft_openpi.yaml), openpi data config pi05_mvtoken.