Olmo's Kitchen: Probing a Model's Behaviour Across Various Checkpoints.

Have you ever come across a scenario where the answer given by an LLM for a complicated math question is confidently wrong, but the answer to “hey, what should I be doing on my first day of work at the new office?” sounds like exactly what you need to hear? Or vice versa? Can we say that the training set domain or training stage domain influences the model’s traits and behaviour for off-domain knowledge and instructions? I began testing this with the open-source checkpoints of the Olmo 3 7B Think model. This model has three training stages in its pipeline, namely supervised fine-tuning (SFT), direct preference optimization(DPO), and reinforcement learning with verifiable rewards(RLVR), available publicly. Now hold up. What does all that even mean? Consider the example of training an amateur chef. The chef is first made to work under the guidance of the Gordon Ramsey of the restaurant. They are given a rulebook and asked to follow, copy, and learn everything that the expert in the field does. The chef becomes a helpful assistant. Now they know how to cook, but are unaware of the customers’ preferences. Two plates of the same dish have different amounts of salt in them. The chef is t