vla-rft-world-model-reinforcement-robotics

name	vla-rft-world-model-reinforcement-robotics
title	VLA-RFT: Reinforcement Fine-Tuning via World Model Simulation
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2510.00406
keywords	["VLA","world-models","reinforcement-learning","robotics","efficiency"]
description	Fine-tune Vision-Language-Action models using learned world models as simulators, eliminating costly real-world or physics-simulation RL. Train robust robot policies in 400 steps via GRPO with model-generated verified rewards.

VLA-RFT: Reinforcement Fine-Tuning via World Model Simulation

VLA-RFT addresses the sample inefficiency of robot learning by using a lightweight learned world model (138M parameters) as a simulator to generate verified rewards for policy optimization. This eliminates sim-to-real gaps and real-world cost while enabling rapid fine-tuning.

Core Architecture

• Lightweight world model: 138M-parameter model predicting next visual frames
• Data-driven simulator: Uses model predictions to compute rewards without physics engine
• GRPO-based RL: Advantage-based optimization on simulated trajectories
• Verified rewards: Geometric and kinematic metrics (GIoU, trajectory distance)
• Rapid convergence: 400 training steps sufficient for task learning

Implementation Steps

Train lightweight world model for robotic prediction:

# Build world model from demonstration data
from vla_rft import WorldModel, VLAPolicy, RLTrainer

world_model = WorldModel(
    vision_encoder="clip",
    prediction_heads=["next_frame", "action_feasibility"],
    model_size="lightweight",  # 138M parameters
    prediction_horizon=1  # single-step prediction
)

# Train on demonstration trajectories
world_model.train_on_demonstrations(
    data=robot_demonstrations,  # ~100-200 trajectories
    learning_rate=1e-4,
    num_epochs=10,
    reconstruction_loss="mse",
    action_feasibility_loss="bce"
)

Execute VLA reinforcement fine-tuning with world model rewards:

# Setup VLA policy and RL trainer
vla_policy = VLAPolicy(
    base_model="pretrained_vla",  # e.g., Qwen2.5-VL-7B
    action_space="continuous",
    device="cuda"
)

rl_trainer = RLTrainer(
    policy=vla_policy,
    world_model=world_model,
    algorithm="GRPO",
    num_rollouts=,
    num_training_steps=
)


 ():
    
    
    predicted_frames = world_model.predict_frames(
        initial_frame=trajectory.initial_image,
        actions=trajectory.actions
    )

    
    final_predicted_frame = predicted_frames[-]
    predicted_object_bbox = extract_bbox(final_predicted_frame)
    giou_reward = compute_giou(
        predicted=predicted_object_bbox,
        target=target.bbox
    )

    
    predicted_path = extract_trajectory(predicted_frames)
    traj_distance = compute_path_distance(
        actual=predicted_path,
        optimal=target.optimal_path
    )

    
    total_reward =  * giou_reward +  * ( - traj_distance)
     total_reward


 step  (num_training_steps):
    
    trajectories = rl_trainer.rollout_batch(
        num_rollouts=,
        task=current_task
    )

    
    rewards = []
     trajectory  trajectories:
        reward = compute_reward(trajectory, current_task.target)
        rewards.append(reward)

    
    loss = rl_trainer.compute_grpo_loss(
        trajectories=trajectories,
        rewards=rewards
    )

    loss.backward()
    optimizer.step()

     step %  == :
        ()

()