How to Fine-Tune a VLA Model on Custom Robot Data

VLA models expect data in specific formats. OpenVLA uses RLDS (TensorFlow Datasets) with episodes containing: observation images (256x256 RGB), language instructions (tokenized text), and action vectors (7-DoF delta end-effector poses). Octo uses a similar RLDS format but with configurable observation and action spaces.

Convert your dataset to the expected format: (1) Resize images to the model's expected resolution (256x256 for OpenVLA, configurable for Octo). Use bilinear interpolation and preserve the aspect ratio by center-cropping if necessary. (2) Normalize actions to the model's expected range. OpenVLA expects actions in [-1, 1] with a specific scaling: position deltas scaled by workspace size, rotation deltas in radians, gripper as binary open/close. Compute normalization statistics (mean, std) from your dataset and apply z-score normalization, then clip to [-1, 1]. (3) Write language instructions for each task. Every episode needs a text instruction (e.g., 'pick up the red mug and place it on the coaster'). If your demonstrations do not have language annotations, write task-level instructions and apply them to all episodes of that task.

Validate the converted dataset by loading 10 random episodes and visually inspecting: images are correctly resized without distortion, actions correspond to the observed robot motion in the images, language instructions match the demonstrated behavior, and episode lengths are within the expected range. Run the VLA model's built-in dataset validation script if available.

Split the dataset: 90% training, 10% validation. Use stratified splitting to ensure each task and condition is represented in both splits. Never include validation episodes in training — overfitting to a small fine-tuning dataset is a real risk with VLA models.

Start from the model's recommended fine-tuning configuration and adjust based on your dataset size and compute budget.

For OpenVLA with LoRA: learning rate 2e-5, LoRA rank 32, LoRA alpha 64, target modules (q_proj, v_proj, k_proj, o_proj), batch size 8 per GPU (32 total across 4 GPUs), warmup steps 100, total training steps = dataset_size * 50 / batch_size (approximately 50 epochs for small datasets), weight decay 0.01, gradient accumulation steps 2 if batch size is limited by VRAM. For full fine-tuning: learning rate 1e-5 (lower than LoRA to prevent catastrophic forgetting), all other parameters similar but expect 2-3x longer training time.

For Octo: learning rate 3e-4, batch size 256 (uses smaller model, can handle larger batches), cosine learning rate schedule with warmup, training for 50,000-200,000 steps depending on dataset size, EMA (exponential moving average) of model weights with decay 0.999.

Critical: implement early stopping based on validation loss. VLA fine-tuning on small datasets (< 500 episodes) is prone to overfitting. Monitor validation loss every 500 steps and stop training when validation loss has not improved for 3 consecutive evaluations. Save checkpoints at each evaluation point so you can roll back to the best one.

Set up Weights & Biases or TensorBoard logging to track: training loss, validation loss, learning rate schedule, gradient norms (to detect instability), and action prediction accuracy on the validation set. If gradient norms spike above 10, reduce the learning rate or increase gradient clipping threshold.

Launch the training job on your GPU cluster (4-8 A100s for OpenVLA, 1-2 A100s for Octo). Use PyTorch Distributed Data Parallel (DDP) for multi-GPU training. Monitor the training run for common issues.

Loss plateau after initial drop: if the training loss drops quickly in the first 1,000 steps then plateaus, the learning rate may be too low or the LoRA rank too small. Try increasing the learning rate by 2x or the LoRA rank from 32 to 64. If the loss oscillates without consistent decrease, the learning rate is too high — reduce by 2-5x.

Catastrophic forgetting: if validation loss on a held-out set of pretraining-distribution tasks increases while fine-tuning loss decreases, the model is forgetting its pretrained knowledge. Mitigate by: reducing the learning rate, using LoRA instead of full fine-tuning, or mixing 10-20% pretraining data into the fine-tuning batch (replay buffer). OpenVLA's codebase supports a replay buffer ratio parameter.

VRAM out-of-memory: reduce batch size per GPU, enable gradient checkpointing (trades compute for memory), use mixed-precision training (fp16 or bf16), or reduce LoRA rank. For OpenVLA on 4x A100 (80GB), the maximum batch size with LoRA is approximately 8 per GPU with gradient checkpointing enabled.

Training time estimates: OpenVLA LoRA fine-tuning on 200 episodes takes approximately 4-6 hours on 4x A100. Full fine-tuning on 2,000 episodes takes approximately 24-48 hours on 8x A100. Octo fine-tuning on 500 episodes takes approximately 6-12 hours on 2x A100. Plan your compute budget accordingly and set up automated checkpoint saving every 30 minutes so you do not lose progress if the job crashes.

After training completes, select the checkpoint with the lowest validation loss (not the final checkpoint, which may be overfitted). Convert the LoRA adapter to a merged model if needed for inference efficiency.

Evaluate the fine-tuned VLA in simulation first (fast iteration), then in the real world (ground truth). For simulation evaluation, set up the task in MuJoCo or Isaac Gym with the same objects, positions, and instructions used in the training data. Run 100+ episodes per task and compute success rate with confidence intervals.

Compare against baselines: (1) the pretrained model without fine-tuning (to measure the improvement from fine-tuning), (2) a task-specific Diffusion Policy trained on the same data (to compare VLA fine-tuning against a non-VLA approach), and (3) the fine-tuned model at different checkpoint epochs (to verify that early stopping selected the best checkpoint).

For real-world evaluation, follow the protocol from the evaluation guide: 50+ trials per condition, controlled variation across object positions and lighting, failure mode analysis. Key VLA-specific evaluation dimensions: (1) language generalization — does the model respond correctly to paraphrased instructions? Test with 5 paraphrases of each training instruction. (2) Visual generalization — does the model handle objects not seen during fine-tuning? Test with 3-5 novel objects in the same category. (3) Spatial generalization — does the model handle object positions outside the training distribution? Test at 5 positions not included in training.

Compute inference latency: VLA models are computationally expensive. OpenVLA runs at approximately 3-5 Hz on a single A100, which may be too slow for fast manipulation tasks requiring 10+ Hz control. Measure the exact inference frequency on your deployment hardware and verify it meets your task's control frequency requirements. If inference is too slow, consider: quantization (INT8 reduces model size by 4x and improves throughput by 2x), batched inference, or distillation to a smaller model.

Deploy the fine-tuned VLA as a real-time robot controller. The deployment pipeline must handle: image preprocessing (resize, normalize), model inference, action denormalization, and robot command execution within the control loop period.

Build the inference server as a separate process from the robot controller, communicating via shared memory or a low-latency RPC (gRPC with Unix domain sockets, < 1ms overhead). The inference server loads the model, receives observation images and language instructions, runs inference, and returns denormalized action vectors. The robot controller sends observations at the control frequency and executes the returned actions.

Optimize inference speed: (1) Quantize the model to INT8 using PyTorch's dynamic quantization or NVIDIA's TensorRT — this typically doubles throughput with < 2% accuracy loss. (2) Use CUDA graphs to eliminate kernel launch overhead for repeated inference calls. (3) Pre-allocate GPU memory for input tensors to avoid allocation overhead. (4) If using LoRA, merge the adapter weights into the base model before deployment to eliminate the adapter overhead.

Handle inference latency with action chunking: instead of predicting one action per inference call, predict a chunk of 8-16 future actions. Execute the first action immediately and continue executing subsequent actions in the chunk while the next inference call is in progress. This converts the effective control frequency from the inference frequency (3-5 Hz for OpenVLA) to the robot's native control frequency (50-500 Hz) by interpolating between predicted actions.

Implement safety wrappers around the VLA's predicted actions: clip actions to safe ranges, enforce joint velocity limits, check for workspace boundary violations, and implement an emergency stop triggered by force/torque thresholds. The VLA model has no inherent safety constraints — it will happily predict actions that drive the robot into a wall if the training data did not include sufficient negative examples.