How can a small language model learn open conversation?

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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.

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Welcome to the forum!

This sounds interesting, but I think it would help if you clarified what model and training setup you are using. The title mentions GPT-5 capability, the tags include GPT-4, but the post sounds like you are training a small local language model.

Since advice can change a lot depending on the base model, model size, training method and dataset type, could you share a bit more about those?

I don’t want to pretend expertise without enough context, but narrowing that down would probably help others give more useful advice.

3

So I started with ChatGPT 2 base model from GitHub, which, despite the claims, was an empty shell. I want to grow the model into a large model overall. The base model was the source for the body upgrade:
“source_layers”: 12, (old body)
“target_layers”: 24, (new body)
“source_embd”: 768,
“target_embd”: 1024,
“source_heads”: 12,
“target_heads”: 16,
“source_ctx”: 1024,
“target_ctx”: 1024,
with a total vocab of ~120k tokens over all. I have very strongly grounded all the new and old tokens in definitions and examples. Yet, as my original post stated, I cannot seem to cross that line.

The goal is to make the model have the same cumulative communicational skill as ChatGPT 3+, yet I cannot do that if the model cannot combine all the education into a communication matrix.

4

Thanks for clarifying.

Since this seems to be about training a small local language model rather than using GPT-5 directly, I adjusted the topic title and tags so it’s easier for people with relevant model-training experience to find it.

The earlier title made me think this was more directly about GPT-5 capability, so hopefully this makes the topic clearer.

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Thank you.
Using a current model, ChatGPT 5+ is great an all. I want to train a local model for my own private research, etc. To see what is possible, but I cannot do that if I cannot get it to cross that communication line.