Evocua Agent Learning

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)
            self.agent.learn_from(trajectory)

        # Evaluate on real benchmarks (OSWorld)
        real_score = self.evaluate_on_real_tasks()
        return real_score

Track agent improvement across iterations. Real performance on OSWorld improved from prior approaches to 56.7% success rate through multiple rounds of synthetic task generation and refinement.

Key Results

• Achieves 56.7% success on OSWorld benchmarks
• Outperforms previous open-source and closed-weight models
• Scales through synthetic experience generation rather than manual annotation
• Enables autonomous continuous improvement cycles

Research Context

This paper addresses the challenge that manual task curation doesn't scale for training generally-capable computer-use agents. By automating task generation, agents can improve through iterative learning loops similar to human learning through varied experiences.