We introduce a neural generator that produces valid Lean~4 expressions directly at the kernel level, bypassing Lean's elaboration process. To train the generator, we implement Lean's core type checking in Python and integrate it into a reinforcement learning environment that assigns rewards for well-typed subexpressions. We evaluate the model's ability to learn and adapt within this environment.