Post #3030820

@tao@mathstodon.xyz I'm developing a new paradigm, called generator paradigm, also generative learning (GL) Would be happy to know your view: (A,W) = G(z), G ∈ G_type, type ∈ {symbolic, neural, RL-based, procedural, neuro-symbolic, . . . }. where: - z is a semantic seed encoding the task, environment, or behavior description. - A is the architecture, specifying the computational graph, structure, or symbolic program of the model. - W are the parameters (e.g., weights, rules, constants) instantiated within A. - G belongs to a class of generators G_type, indexed by their realization type (e.g., symbolic, neural, procedural, RL-based, etc.). It sidesteps "symbolic traps," like the EA used in AlphaEvolve, but is even better