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

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Model Card

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

This repository contains the uploaded 30,000-step checkpoint from the 1025_libero4in1_qwenfast run. It uses the StarVLA QwenFast framework and the libero_all mixture, which combines LIBERO Object, Goal, Spatial, and LIBERO-10 with equal registry weights. The policy tokenizes continuous action chunks with FAST and predicts the resulting action tokens autoregressively with the VLM; it does not run a diffusion sampler.

Model details

Item	Published configuration
Framework	StarVLA `QwenFast`
VLM	Local snapshot named `Qwen2.5-VL-3B-Instruct-Action`; revision not recorded
Action encoding	FAST tokenizer over continuous action chunks
Training objective	Autoregressive cross-entropy over mapped action tokens
Inference	VLM token generation followed by FAST-token decoding
Action dimension	7
Numeric policy-state input	None in the checked `QwenFast` forward and inference paths; YAML `state_dim: 7` is unused there
Packaged raw state statistics	8D under `franka`; dataset artifact only for this policy path
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 QwenFast implementation does not construct a DINO/DiT head or read numeric state: it trains the VLM on FAST action tokens. Do not turn the packaged 8D dataset state statistics into a model input that the checkpoint was not trained to consume.

Training data

libero_all resolves to four LeRobot datasets:

—libero_object_no_noops_1.0.0_lerobot
—libero_goal_no_noops_1.0.0_lerobot
—libero_spatial_no_noops_1.0.0_lerobot
—libero_10_no_noops_1.0.0_lerobot

The packaged dataset_statistics.json contains the franka normalization entry used at inference.

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

summary.jsonl lists 10K, 20K, and 30K. It does not contain success rates.

Evaluation status

Missing for this artifact. The Hub repository contains no LIBERO rollout logs, per-suite success table, seed list, or aggregate result. The StarVLA paper reports 30K LIBERO results for Qwen3-VL-4B variants, but this checkpoint uses a Qwen2.5-VL-3B backbone, so those values are not transferred to this card.

For a future comparable report, the project protocol evaluates all 10 tasks in each suite with 50 episodes per task (500 trials per suite) and should record the evaluator revision and seed.

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-FAST-LIBERO-4in1 \
  --local-dir Qwen2.5-VL-FAST-LIBERO-4in1

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

Run the simulator in a separate environment using the StarVLA LIBERO guide and select normalization key franka.

Intended use and limitations

This checkpoint is intended for LIBERO simulation research with a single RGB view and 7D delta-joint-position actions. Its runtime action representation is autoregressive FAST token generation, even though the legacy YAML labels an unused action-model block DiT-B. No public score, physical-robot validation, or safety guarantee is provided.