Looking for curriculum plans for to take a model from initialization to check GPT 5 capability

I am building a small local language model and looking for advice on curriculum design. My goal is to move the model toward strong natural-English communication and understanding, eventually reaching the first usable level of ChatGPT-like conversational skill. So far, I have trained it through several curriculum stages: Baseline stabilization Definition grounding Definition reinforcement Truth verification Right/wrong verification Target-lock recall Definition boundary control Reverse definition lookup Definition/example separation Turn-type recognition Reply logic Role/relation binding Location bridge training Contrast repair Conversation response control Turn examination and response assembly Contextual turn-purpose control Context contrast and purpose selection Request-vs-meaning control Social-turn-vs-definition control Correction and repair control Not-given response control The model has improved greatly in isolated logic lanes. It can learn individual training families very quickly, and its loss drops very low during training. The problem is that it still struggles to combine those learned lanes into stable open conversation. It can know the correct pieces, but it does not reliably assemble them into natural communication. I am trying to help it cross the line from “trained response families” into actual conversational understanding. For anyone who has worked on curriculum training, small-model post-training, dialogue control, or staged language learning: What curriculum steps helped your model start combining learned skills into usable conversation? Should I focus next on contextual understanding, sentence-role training, parts of speech, multi-turn dialogue, preference pairs, replay/retention mixing, or something else? I am especially interested in practical dataset structure, ordering, evaluation advice, or just advice on how to reach my goal.