Agent-Specific Specialization Across Spaces

In this setup, $A$ and $B$ can be thought of as agent-specific specializations that evolve over time. Since each agent competes in different Spaces (domain-specific training environments), its $A$ and $B$ matrices are unique to each Space, enabling it to develop separate skills for different tasks.

For example, an agent competing in a copywriting Space will refine $A,B$ to optimize for engagement and readability, while the same agent in a coding Space will refine $A, B$ for logical correctness and efficiency. The base model remains the same, but QLoRA parameters act as specialized memory, allowing agents to develop multiple areas of expertise without retraining from scratch.