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topreward-qwen3vl-4b-nf4

Monitor robotic task progress with Qwen3-VL-4B VLM zero-shot reward model.

Updated Jul 5, 2026
Search Tags:#progress monitoring#reward model#OpenRAL#robotic#Qwen3-VL-4B
One-click install
npx skills add https://github.com/bensonlee5/openral --skill topreward-qwen3vl-4b-nf4
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: topreward-qwen3vl-4b-nf4
Source: https://github.com/bensonlee5/openral/tree/main/rskills/topreward-qwen3vl-4b-nf4
Command: npx skills add https://github.com/bensonlee5/openral --skill topreward-qwen3vl-4b-nf4

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires Qwen/Qwen3-VL-4B-Instruct, openral, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This rSkill provides a zero-shot reward model to monitor task progress in robotic tasks, using Qwen3-VL-4B VLM for likelihood estimation of task completion.

Core Features & Use Cases

  • Zero-Shot Reward Model: Evaluates task likelihood using Qwen3-VL-4B VLM without fine-tuning.
  • Progress Monitoring: Provides per-frame progress signals for tasks.
  • Use Case: A robot performing a picking task can use this rSkill to gauge its progress based on the likelihood of successfully completing the task as predicted by the model.

Quick Start

Install the rSkill: ral skill install hf://OpenRAL/rskill-topreward-qwen3vl-4b-nf4

Frequently Asked Questions about topreward-qwen3vl-4b-nf4

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

FAQPage Schema
How do I monitor robotic task progress with a zero-shot reward model?▼

Robotic task progress monitoring estimates completion likelihood using a zero-shot reward model with Qwen3-VL-4B VLM. It evaluates visual inputs without fine-tuning to provide per-frame progress signals for tasks like picking.

Can I use Qwen3-VL-4B for robotic progress estimation without fine-tuning?▼

Yes, Qwen3-VL-4B evaluates task completion likelihood as a zero-shot reward model without fine-tuning. It directly analyzes visual inputs to provide per-frame progress signals for robotic task execution.

Does OpenRAL support NF4 quantization for VLM-based reward models?▼

OpenRAL supports NF4 quantization for VLM-based reward models by running the Qwen3-VL-4B-Instruct model. This Skill deploys within the OpenRAL runtime to enable efficient robotic progress monitoring.

What's the best way to evaluate task completion likelihood in robotic execution?▼

Evaluating task completion likelihood in robotic execution is best achieved using a zero-shot VLM reward model. This Skill applies Qwen3-VL-4B to provide per-frame progress signals for critical robotic tasks.

What are the limitations of using a quantized VLM for robotic progress monitoring?▼

Using a quantized VLM for robotic progress monitoring requires the OpenRAL runtime with NF4 quantization support and Qwen3-VL-4B. Progress estimation relies entirely on zero-shot likelihood evaluation without task-specific fine-tuning.

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