evocua-agent-learning

name	evocua-agent-learning
title	EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.15876
keywords	["agent-learning","synthetic-experience","computer-use","evolution","reinforcement-learning"]
description	Train autonomous agents to use computers by generating synthetic task experiences and iterating on them, achieving 56.7% success on OSWorld benchmarks through scalable experience-driven optimization. Use when you need agents that autonomously learn complex computer interaction patterns without manual task curation.

EvoCUA: Evolving Computer Use Agents

This skill enables training computer-use agents that autonomously generate diverse synthetic tasks and learn through iterative cycles, significantly improving performance on real-world benchmarks.

When to Use

• Building agents that interact with computer interfaces autonomously
• Creating systems that improve through self-generated experience loops
• Optimizing agent performance on complex desktop/web interaction tasks
• Implementing scalable agent training without extensive manual annotation

When NOT to Use

• Tasks requiring immediate inference (training takes multiple iterations)
• Domains where synthetic experience won't transfer to real tasks
• Simple rule-based automation (overkill for simple workflows)
• Systems where you already have extensive human-annotated training data

Key Concept

EvoCUA evolves agents through a feedback loop: generate synthetic tasks → train agent → evaluate performance → refine task generation. This removes the bottleneck of manual task creation.

The approach combines two stages:

1. Task Evolution: Autonomously generate diverse synthetic computer use tasks
2. Agent Learning: Iteratively train the agent on evolved tasks, improving real-world performance

Implementation Pattern

Generate diverse synthetic tasks and create a feedback loop for agent training:

# Pseudocode for EvoCUA training loop
class EvoCUA:
    def __init__(self, agent, task_generator):
        self.agent = agent
        self.task_generator = task_generator

    def train_iteration(self, num_synthetic_tasks=100):
        # Generate diverse synthetic tasks
        tasks = self.task_generator.create_tasks(num_synthetic_tasks)

        # Train agent on synthetic tasks
        for task in tasks:
            trajectory = self.agent.execute(task)
            .agent.learn_from(trajectory)

        
        real_score = .evaluate_on_real_tasks()
         real_score