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yeeeiii111/
pi05-pnp-left-500-60k

Name: pi05-pnp-left-500-60k
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pi0.5 finetune — pnp_left_500 (step 60000)

Openpi pi0.5 finetune on a 500-episode left-arm pick-and-place LeRobot dataset. This is the step 60000 checkpoint of a 100000-step run.

Training

Item	Value
Base model	pi0.5 (`pi05_base`)
Framework	openpi (PyTorch trainer)
Dataset	502 episodes / 154,059 frames / 30 fps
Batch size (global)	16
Optimizer	AdamW, peak_lr=5e-5, cosine decay, warmup=500
FSDP devices	4
action_dim	54
action_horizon	50
max_token_len	256
freeze_filter	LLM frozen except `llm__1` and LoRA paths
Total steps	100000
This checkpoint	step 60000

Why step 60000

Loss trajectory (5k-step bucket means):

step range	loss	grad_norm
15k–20k	0.0236	0.281
20k–25k	0.0204	0.260
40k–45k	0.0149	0.213
60k–65k	0.0133	0.193
80k–85k	0.0115	0.178
95k–100k	0.0108	0.170

Step 60000 sits in the flattening region — most of the improvement over 20k is captured (~35% lower loss), and further training only shaves ~20% more.

model.safetensors — model weights only (7.0 GB)
metadata.pt — training metadata
assets/pnp-left-500-lerobot/norm_stats.json — dataset normalization statistics

optimizer.pt is not included (would add 13 GB and is only needed for warm resume).

Provenance

W&B run: better_guidance/pickplace-pi/8affq7ty (pi05_pnp_left_500_20260622_154344)

yeeeiii111/pi05-pnp-left-500-60k

pi0.5 finetune — pnp_left_500 (step 60000)

Training

Why step 60000

Contents

Provenance

yeeeiii111/
pi05-pnp-left-500-60k