If you’ve ever tried to automate your own software workflows using AI, you’ll know the hard part isn’t reasoning — it’s clicking the right button in a sea of ambiguous icons, drop-downs, and obscure UIs. For agents tasked with navigating GUIs like humans do, the real challenge isn’t logic — it’s context.
Enter SEAgent: a self-evolving computer-use agent that doesn’t just learn to operate software — it teaches itself how to learn, using nothing but screenshots, feedback from its own past mistakes, and a clever curriculum.
The Core Idea: From Demonstration-Driven to Experience-Driven
Most current Computer Use Agents (CUAs) rely on human demonstrations or curated datasets. This is expensive and fragile — new software or interface updates break them. SEAgent flips this paradigm by learning entirely from experience — no human supervision, no labeled data, no demonstrations.
Its goal is ambitious: master unfamiliar software (like VLC, GIMP, or LibreOffice) through a self-supervised loop of task generation, exploration, evaluation, and reward.
Anatomy of a Self-Evolving Agent
At the heart of SEAgent lies a three-part loop:
| Component | Function |
|---|---|
| Actor Model | Tries to solve tasks by interacting with the GUI (e.g. clicking, typing) |
| World State Model (WSM) | Evaluates every step, telling the agent where it succeeded, failed, or wasted time |
| Curriculum Generator | Designs new tasks, growing in complexity as the agent improves |
What’s remarkable is the use of LVLMs (Large Vision-Language Models) not just as perception engines, but also as judges and teachers — analyzing trajectories and generating tasks.
Learning by Doing — and by Failing
SEAgent’s learning process looks like this:
- Start with basic GUI tasks like “open a new file.”
- Attempt them using a pretrained agent (e.g., UI-TARS).
- WSM scores each step — not just the final outcome.
- For correct actions: apply Group Relative Policy Optimization (GRPO) to encourage them.
- For incorrect ones: use Adversarial Imitation Loss to actively suppress failure-prone behaviors.
- Curriculum Generator observes what’s learned, updates a dynamic software guidebook, and proposes more challenging tasks.
This loop repeats over multiple phases, resulting in a steep, autonomous learning curve.
From Specialist to Generalist — And Beyond
One of SEAgent’s most important contributions is how it reconciles the generalist vs. specialist tradeoff:
- Training one generalist agent across multiple software platforms often leads to mediocre performance.
- Training software-specific specialists gives better results — but lacks versatility.
SEAgent solves this through a specialist-to-generalist pipeline:
- Train individual specialists on each software (e.g., GIMP, VLC).
- Extract successful trajectories.
- Fine-tune a new generalist model using these specialist traces.
- Reinforce this generalist via SEAgent’s RL loop.
Result: A generalist agent that outperforms even the specialist ensemble.
Performance: A 3x Leap Without Human Labels
Let’s talk numbers. On the challenging OSWorld benchmark (GIMP, VLC, LibreOffice, etc.), SEAgent achieved the following:
| Model | Avg. Success Rate |
|---|---|
| GPT-4o | 7.1% |
| UI-TARS (baseline) | 11.3% |
| WebRL / DigiRL (specialist RL) | ~21.8% |
| SEAgent (specialist) | 32.2% |
| SEAgent (specialist-to-generalist) | 34.5% |
This is not just a performance boost — it’s a paradigm shift. A self-evolving agent, trained without human supervision, achieves 3x improvement over the strongest open-source baseline.
Why It Works: Better Rewards, Better Tasks
Two breakthroughs make this possible:
-
World State Model (WSM)
- Built on Qwen2.5-VL, fine-tuned with GPT-4o labels.
- Scores each action step-by-step using screenshots.
- Matches commercial models like GPT-4o in evaluation accuracy.
-
Curriculum Generator
- Grows task complexity based on the agent’s demonstrated skills.
- Maintains a software guidebook — like memory — that informs next tasks.
- Outperforms other instruction generation baselines like WebRL and NNetNav, especially in out-of-domain settings.
Limitations: Still Not Ready for Photoshop or Excel Macros
While SEAgent excels at software requiring 5–20 step tasks, it’s not yet optimized for complex, long-horizon workflows. It also depends on a simulated reward model (WSM), which might not fully reflect real-world outcomes — for instance, it may mark a form as complete based on GUI state, without checking for actual data correctness.
Future work must address:
- Sparse reward handling in complex environments
- Multimodal memory over hours of interaction
- Integrating domain knowledge for semantic correctness
Why This Matters for the Enterprise
SEAgent provides a blueprint for AI agents that adapt to enterprise software without pre-training, without labeling, and without engineers rewriting automation rules.
That’s a game-changer.
Imagine internal AI tools that can:
- Learn your ERP or CRM system in hours
- Build task libraries from scratch
- Help automate onboarding, reporting, or dashboard creation
No more waiting on RPA developers to support your workflows.
SEAgent shows that generalist UI agents are within reach — but you get there by starting with specialists, and letting them teach the generalist.
Cognaptus: Automate the Present, Incubate the Future.