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StarVLA/Qwen2.5-VL-OFT-LIBERO-4in1

sourceHugging Faceupdated 23d agoView on Hugging Face

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StarVLA QwenOFT Qwen2.5-VL-3B for LIBERO 4-in-1 (30K)

This repository publishes one 30,000-step checkpoint from the 1025_libero_all_qwengoft run. The trainer target in config.yaml is 80K and summary.jsonl lists checkpoints through 50K, but no 40K or 50K weight file is present; the model available to download is the 30K artifact.

Model details

Item	Published configuration
Framework	StarVLA `QwenOFT`
VLM	Local snapshot named `Qwen2.5-VL-3B-Instruct`; revision not recorded
Action features	Hidden states at repeated action-placeholder tokens
Action head	Two-block residual MLP with direct L1 regression
Action dimension	7
Numeric policy-state input	None in the checkpoint-era `QwenOFT` action-head path; YAML `state_dim: 7` is not an MLP input
Packaged raw state statistics	8D under `franka`; not evidence of a direct policy-state input
Action representation / chunk	`delta_qpos` / 8 steps
Camera input	`image_0`, 224 x 224
Uploaded checkpoint	`checkpoints/steps_30000_pytorch_model.pt`

The legacy YAML retains dino, action_model_type: DiT-B, diffusion, and state_dim: 7 fields. The selected QwenOFT implementation does not build those components: its action-model factory constructs a residual MLP, consumes VLM action-placeholder hidden states, and optimizes L1 action loss. Later opt-in code can serialize state into the instruction, but no numeric state is fed to this checkpoint's MLP head. The stale fields must therefore not be presented as runtime architecture.

Training data

libero_all combines equal-weight, no-noop LeRobot versions of LIBERO Object, Goal, Spatial, and LIBERO-10. The packaged franka statistics provide the action normalization contract; their state entry comes from the dataset and does not imply direct state conditioning for this policy.

Training details

Setting	Value in `config.yaml`
Intended maximum steps	80,000
Released step	30,000
Per-device VLA batch size	16
Gradient accumulation	1
Warm-up steps	5,000
Base / interface / action LR	`3e-5` / `1e-5` / `1e-4`
Optimizer	AdamW, betas `(0.9, 0.95)`, epsilon `1e-8`
Scheduler	Cosine with minimum LR `1e-6`
VLA / VLM loss scale	`1.0` / `0.1`
Packaged `freeze_modules`	Boolean `true`; the public trainer only accepts a comma-separated string, so this does not select modules to freeze
Gradient checkpointing / mixed precision	Enabled / enabled
Seed	42

Evaluation status

Missing for this artifact. There are no LIBERO rollout logs or quantitative results in the Hub repository. The Qwen3-VL-4B OFT numbers in the StarVLA paper refer to a different backbone/release and are not assigned to this Qwen2.5-VL checkpoint.

The expected reproducible report is 50 episodes for each of 10 tasks in every suite, with Object, Goal, Spatial, LIBERO-10, and their unweighted average reported separately.

Files

text

config.yaml
dataset_statistics.json
summary.jsonl
checkpoints/
└── steps_30000_pytorch_model.pt

Loading and evaluation

bash

huggingface-cli download StarVLA/Qwen2.5-VL-OFT-LIBERO-4in1 \
  --local-dir Qwen2.5-VL-OFT-LIBERO-4in1

export CKPT=$PWD/Qwen2.5-VL-OFT-LIBERO-4in1/checkpoints/steps_30000_pytorch_model.pt
python deployment/model_server/server_policy.py \
  --ckpt_path "$CKPT" \
  --port 10093 \
  --use_bf16

Use the StarVLA LIBERO guide and normalization key franka for the simulator client.

Intended use and limitations

This is a LIBERO simulation research checkpoint. Its exact VLM revision, benchmark score, and historical code revision are not fully recorded. The public OFT implementation establishes the MLP/L1 action-head structure, while the stale YAML fields should not be used to instantiate a DiT. It has not been validated for other embodiments or physical robots and is not safety-tuned.