Parameter	Value
Base model	pi0.5 (PaliGemma 2B + Gemma 2B action expert)
Total parameters	~3.35B
Action dimension	32
Action horizon	10
Batch size	16
Training steps	5,000
Learning rate	Cosine decay: warmup=500, peak=5e-5, end=5e-6
Optimizer	AdamW (gradient clip norm=1.0)
GPUs	8x NVIDIA A100
Normalization	Quantile normalization

Checkpoints

Step 3000: loss = 0.0060
Step 4000: loss = 0.0040
Step 4999

Loss Curve

Step	Loss
0	0.0835
500	0.0154
1000	0.0125
1500	0.0105
2000	0.0084
2500	0.0070
3000	0.0060
3500	0.0051
4000	0.0040
4500	0.0035

Part of Mode Editing Research

This checkpoint is part of the "Don't Filter Your Data, Edit Your Policy" project (CoRL 2026), investigating post-hoc behavior mode editing for robot policies using Classifier-Guided Distillation (CG-Distill).

haohw/pi05_real_pb_from_right

pi05_real_pb_from_right

Task

Training Configuration

Checkpoints

Loss Curve

Part of Mode Editing Research

haohw/
pi05_real_pb_from_right