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Miical/pi05-libero-10-sft-step-200

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Model Card

PI0.5 LIBERO-10 SFT — Step 200

This is a deliberately undertrained PI0.5 checkpoint fine-tuned on the complete official LIBERO-10 dataset. It is intended as a reproducible starting policy for measuring how much downstream reinforcement learning, particularly ReCap, improves long-horizon task success rather than as a fully trained SFT release.

The checkpoint is an upstream-native, merged PI0.5 policy exported by verl-vla. It does not require a verl-vla checkpoint wrapper for inference.

Lineage

—Base model: `Miical/pi05-base`
—Training dataset: `lerobot/libero_10_image`
—Benchmark: LIBERO-10, all 10 tasks
—Checkpoint: optimizer step 200

Training configuration

Setting	Value
Training method	Supervised fine-tuning (SFT)
Optimizer steps	200
Dataset size	101,469 frame-level samples, 379 episodes
Samples consumed	51,200
Effective epochs	approximately 0.50
Global / mini-batch size	256
Micro-batch size	16
GPUs	8 x NVIDIA H20
Distributed strategy	FSDP2
Model dtype	bfloat16
Learning rate	1e-4
Weight decay	1e-5
LR warmup ratio	0.05
Action horizon	10
DataLoader workers	8
Seed	42

One complete pass over this dataset is 396 optimizer steps with the configured batch size. This checkpoint was saved at step 200, about halfway through the first epoch. It therefore provides a moderately capable but intentionally non-saturated policy for subsequent reinforcement-learning experiments.

LIBERO-10 evaluation

Evaluation used all 10 LIBERO-10 tasks with 10 trials per task, a maximum of 520 environment steps per trial, and 52 policy interactions with a 10-action chunk.

Task	Instruction	Successes	Success rate
0	Put both the alphabet soup and the tomato sauce in the basket	4 / 10	40%
1	Put both the cream cheese box and the butter in the basket	6 / 10	60%
2	Turn on the stove and put the moka pot on it	4 / 10	40%
3	Put the black bowl in the bottom drawer of the cabinet and close it	5 / 10	50%
4	Put the white mug on the left plate and put the yellow and white mug on the right plate	3 / 10	30%
5	Pick up the book and place it in the back compartment of the caddy	8 / 10	80%
6	Put the white mug on the plate and put the chocolate pudding to the right of the plate	6 / 10	60%
7	Put both the alphabet soup and the cream cheese box in the basket	5 / 10	50%
8	Put both moka pots on the stove	0 / 10	0%
9	Put the yellow and white mug in the microwave and close it	2 / 10	20%
Overall	All tasks	43 / 100	43%

These results are from one complete 100-trajectory benchmark run. Per-task estimates use only 10 trials and should not be interpreted as precise population rates.

Intended use

This model is intended as an incompletely trained initial policy for validating whether ReCap or another reinforcement-learning method improves aggregate and per-task LIBERO-10 success. In particular, task 8 has no successful trajectory in this evaluation, while the other tasks provide varying amounts of initial capability. For a high-performing SFT baseline or deployment candidate, use a later and more thoroughly evaluated checkpoint instead.

Loading

python

from giga_models import PI0Policy

policy = PI0Policy.from_pretrained(
    "Miical/pi05-libero-10-sft-step-200"
)

With verl-vla, set the model path directly:

yaml

cluster:
  actor_rollout_ref:
    model:
      path: Miical/pi05-libero-10-sft-step-200
      adapter:
        embodiment: libero

License and attribution

This fine-tuned checkpoint inherits the usage and redistribution constraints of the original PI0.5 checkpoint and its underlying components. The PyTorch conversion and verl-vla integration code have their own respective licenses; consult the base model card and upstream projects before redistribution or commercial use.