Base model vs instruction-tuned model: what does the user actually feel?

**Option A is correct.** A base model is the raw output of pre-training, where the only training objective was next-token prediction over a giant corpus of text. Hand it a question and it often does not answer; it continues the text it sees, sometimes by writing what looks like the rest of a forum post or a sibling document. An instruction-tuned model has gone through at least one additional training stage on top of the base. Supervised fine-tuning (SFT) on prompt and response pairs teaches the model to actually answer when asked a question, follow a chat template, and respond in a helpful conversational style. A preference-tuning pass like RLHF or DPO usually follows, which shapes the model to prefer helpful responses and to decline harmful ones. The user-visible difference is the combination of these two behaviours. The instruct model answers your question rather than continuing your sentence, and it refuses categories of unsafe requests rather than complying with them. The wrong options locate the difference in the wrong place. The weights are genuinely different (so B is wrong). Size and training stage are separate concerns (so C is wrong). Safety is learned into the weights, not bolted on at decode time (so D is wrong).

**The right answer is A**, and the precise reason matters. A base model is the output of pre-training, where the objective is next-token prediction over a large heterogeneous corpus. The model learns rich representations of language but is not conditioned to answer questions or follow chat-style prompts. Asked 'What is photosynthesis?', a base model might continue with '... is a process by which plants...' which looks like an answer or with 'this question appears on many biology exams', which is what a sibling document would say. The behaviour is not adversarial; the model is just doing what it was trained for. An instruction-tuned model has had at least one additional post-training stage. The first is supervised fine-tuning on instruction-response pairs, which teaches the model to recognise a chat template, treat the user turn as a request, and emit a response. The second, usually a preference-optimisation pass like RLHF or DPO, shapes the model to prefer helpful, harmless, and honest responses according to a preference signal. After this stage, the model follows chat-style asks reliably and refuses categories of unsafe requests. The weights are genuinely different. The same architecture and starting point produce different parameter values after the post-training passes, which is why a chat template alone cannot turn a base model into a chat model. The distractors get this wrong: B claims the weights are identical, C confuses size with training stage, and D invents a runtime safety classifier when safety in instruct models is learned into the weights through preference tuning. The headline distinction is two behaviours that emerge from post-training: instruction-following and trained refusals.