PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives
PRAXIS introduces a co-evolutionary framework modeling self-improving learning as an interaction between generators, learners, and symbolic archives. It formally analyzes objective drift and stability in nonstationary objectives by coupling adaptive universe selection, gradient optimization, and archive regularization. The research shows that this coupled system stabilizes optimization while ensuring the symbolic archive concentrates on useful programs.