agent0-vl-self-evolution

name	agent0-vl-self-evolution
title	Agent0-VL: Self-Evolving Agent for Tool-Integrated VL Reasoning
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2511.19900
keywords	["Self-Evolving Agents","Tool-Grounded Verification","Visual Reasoning","Reinforcement Learning","GRPO"]
description	Enable vision-language agents to self-evolve by grounding verification in tool outputs rather than text: implement nested loops where Solver+Verifier generate trajectories and tool-based feedback, then optimize via GRPO using self-generated rewards without external supervision.

Agent0-VL: Self-Evolving Vision-Language Agents

Vision-language agents typically struggle with self-evaluation—they can easily hallucinate confidence scores or generate false critiques without external grounding. This skill demonstrates how to build agents that self-evolve by grounding their verification process in tool-generated evidence, creating a unified loop where reasoning, verification, and self-repair happen through the same agentic mechanism, all optimized via reinforcement learning.

The key innovation is tool-grounded verification: instead of text-based self-evaluation (prone to hallucination), the Verifier evaluates reasoning steps by checking tool outputs, enabling genuine self-correction before policy updates.

Core Concept

Agent0-VL implements a Self-Evolving Reasoning Cycle (SERC) with two nested loops:

1. Inner Loop (Generation & Verification): Solver generates reasoning trajectories with tool calls; Verifier evaluates using tool-generated evidence and produces structured feedback. When confidence is low, self-repair mechanisms correct reasoning before re-execution.

2. Outer Loop (Policy Update): GRPO optimizes the unified policy using self-generated rewards, requiring zero external reward supervision.

The system operates entirely on tool-grounded evidence, avoiding evaluation hallucination common in text-only LLM self-evaluation.

Architecture Overview

• Unified Solver-Verifier Model: Single model with roles for both generation and verification
• Tool-Grounded Verification: Verification uses tool outputs to assess correctness, not subjective text confidence
• Self-Repair Mechanism: On low confidence, agent regenerates and re-executes before moving forward
• Confidence Thresholds: Numerical gates determining when self-repair triggers
• Reward Signal Design: Self-generated rewards from tool-based correctness signals
• GRPO Optimization: Policy gradient RL using self-generated, tool-grounded rewards

Implementation Steps

The self-evolution process cycles through reasoning generation, tool-grounded verification, optional repair, and policy updates.

1. Initialize Unified Solver-Verifier Model

Create model infrastructure supporting both generation (Solver) and verification (Verifier) roles.

def create_solver_verifier_model(base_model_name, max_tokens=):
    
    model = load_vlm_model(base_model_name)

    
    solver_template = 

    
    verifier_template = 

     {
        : model,
        : solver_template,
        : verifier_template,
        : max_tokens
    }