Neurogenesis Reinforcement Learning Language Model

We propose the Neurogenesis Reinforcement Learning Language Model (NRLLM), a research framework in which stable linguistic mastery can trigger endogenous growth of a language agent trained through reinforcement learning. Godrick is the proposed experimental realization. Its defining hypothesis is that an exactly-one-neuron increase in a dynamic hidden structure, conditioned on independently specified mastery criteria, can support subsequent learning without sacrificing previously acquired capabilities. The framework separates content admission, learning, mastery assessment, and structural growth. A beta discernment subsystem evaluates whether authorized material is relevant and sufficiently supported for a target linguistic capability; it does not certify mastery. A future continual-learning stage would use a localhost chat and explicitly permitted internet sources. We formalize a candidate growth gate, outline initialization and plasticity choices, and specify controlled comparisons against fixed capacity, alternative growth triggers, replay, and feature replacement. Earlier work establishes individual components, including one-unit growth, artificial neurogenesis, reinforcement learning, and language-model expansion. The contribution claimed here is a proposed synthesis and falsifiable experimental protocol, not demonstrated superiority or an established claim of historical priority. No implementation results or benchmark measurements are reported.