Koji Operator
Development Progress — Fine-Tuned Instruction Model for CHO Platform
What is Koji?
Koji is the instruction-tuned operator model that powers CHO's decision-making. It translates natural language tasks into structured actions that control the desktop environment, manage memory, execute research, and communicate with users.
Built on an open-weight foundation model, Koji is fine-tuned specifically for agentic workflows—learning to think step-by-step, verify before acting, and chain multiple operations to complete complex tasks.
Development Timeline
Initial training phase. Single-turn instruction following. Basic command execution without multi-step reasoning.
Introduced multi-turn trajectories. Model learns agentic loop: Act → Receive Feedback → Decide Next Step. Natural language prompts.
Production-quality training data with comprehensive scenario coverage. Web development, authentication, deployment, research-first patterns, and multi-file project creation.
Training Approach
| Version | Focus |
|---|---|
| v0.1.0 | Basic instruction drills |
| v0.1.1 | Multi-turn, natural language |
| v0.1.2 | Full coverage, research-first |
Behavioral Improvement
User: "Check my files"
→ Generic response, no action
User: "Check my files"
→ Lists directory, reports results
Capability Coverage
- Core system operations (file, memory, terminal)
- Web development workflows (project scaffolding, components)
- Research-first patterns (learn before implementing)
- Multi-file project creation
- Error recovery and debugging
- Authentication and database integration
- Deployment workflows
- Cross-domain tasks (medical, robotics, science)
Validation
After each training cycle, Koji is tested against real-world scenarios including multi-file projects, research tasks, error recovery, and memory recall to ensure production readiness.
Roadmap
| Phase | Focus |
|---|---|
| Current | Production training, full coverage |
| Next | Real-world fine-tuning from feedback |
| Future | Extended context, longer memory |