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fine-tuning-openvla-oft

Fine-tune and evaluate OpenVLA-OFT policies for continuous action prediction.

Updated May 4, 2026
Search Tags:#evaluation#fine-tuning#robotics#lora#libero#openvla-oft#aloha
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill fine-tuning-openvla-oft-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-openvla-oft
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/openvla-oft
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill fine-tuning-openvla-oft-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps researchers and engineers fine-tune, evaluate, and deploy OpenVLA-OFT policies without losing performance to mismatched checkpoints, training flags, or hardware-specific setup issues.

Core Features & Use Cases

  • Fine-tunes OpenVLA-OFT and OpenVLA-OFT+ with LoRA, continuous action heads, and optional FiLM conditioning.
  • Evaluates policies on LIBERO simulation suites and supports server-client deployment for ALOHA real-world robot control.
  • Guides consistency checks for image streams, proprioception, crop behavior, action chunking, and unnormalization keys.
  • Use it to reproduce paper results, compare task-specific versus combined checkpoints, or debug low-success inference runs.

Quick Start

Ask me to set up, fine-tune, and evaluate an OpenVLA-OFT checkpoint for LIBERO or ALOHA with all training, deployment, and validation flags kept consistent.

Frequently Asked Questions about fine-tuning-openvla-oft

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune OpenVLA-OFT for continuous action prediction on LIBERO?▼

To fine-tune OpenVLA-OFT on LIBERO, apply LoRA and continuous action heads while keeping image streams, proprioception, crop behavior, and unnormalization settings consistent across training and evaluation.

Why does my OpenVLA-OFT inference have low success rates after fine-tuning?▼

Low inference success in OpenVLA-OFT often stems from mismatched training flags, such as incorrect action chunk sizes, unnormalization keys, or FiLM conditioning settings between the checkpoint and evaluation.

Can I deploy OpenVLA-OFT policies for real-world ALOHA robot control?▼

Yes, OpenVLA-OFT supports server-client deployment for ALOHA real-world robot control, requiring consistent image streams, crop behavior, and unnormalization settings to maintain policy performance.

What settings need to match when evaluating OpenVLA-OFT checkpoints?▼

Evaluating OpenVLA-OFT requires consistent LoRA, FiLM, image-stream, proprioception, crop, chunk-size, and unnormalization settings across training, inference, and evaluation to avoid performance drops.

Does OpenVLA-OFT support FiLM conditioning during fine-tuning?▼

Yes, OpenVLA-OFT supports optional FiLM conditioning during fine-tuning, allowing you to adapt robot policies for continuous action prediction while maintaining consistent evaluation flags.

What is the best way to reproduce OpenVLA-OFT paper results?▼

To reproduce OpenVLA-OFT paper results, fine-tune and evaluate robot policies on LIBERO simulation suites or ALOHA deployment, ensuring all LoRA, action chunking, and unnormalization settings remain consistent.

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