Overview: Baseline Model

Justy Cody, we have been throwing around baseline model for almost a year like everyone was born knowing the term. We kept saying, yeah, we should give that its own episode someday… and this is that overdue cleanup.

Cody Good, because baseline is one of those boring phrases that does an absurd amount of work. If you say your new model got seventy five percent accuracy, my immediate reaction is not applause. It's, compared to what?

Justy The mental model I like is: before you brag about how high the ceiling is, mark where the floor is. The baseline is that floor mark. It is the plain version of the thing before you add the clever part.

Cody Yeah. And I would make it even more annoying, because that's my brand. The baseline is the control condition. It is the system you run so you can tell whether your new trick caused the improvement, or whether you just measured a number that looked impressive by itself.

Justy Right.

Cody Imagine a question-answering task. You build a new training method, run the model, and it gets seventy five percent. That sounds good until the unchanged model gets seventy four percent. Then your giant breakthrough is maybe one point, maybe noise, maybe not worth the compute.

Justy So baseline model is not automatically the dumbest possible model. It's the simplest defensible comparison for the claim you're making. If your claim is, my new technique helps, the baseline is what happens without that technique.

Cody Exactly. The formal-ish definition is: a baseline model is a reference model used to judge whether a more complex or modified system is actually better. Sometimes that reference is random guessing. Sometimes it is a majority-class predictor, meaning it always picks the most common answer. Sometimes it is a strong off-the-shelf model with no special changes.

Justy Okay, slow that down with the actual workflow. Like, I have a task, I have data, I have a model. Where does the baseline happen?

Cody You start by making the comparison fair. We did a whole episode on train-test split, episode seven forty-three, but the one-line version is: you train on one chunk of data and test on a separate chunk the model did not get to practice on. That split keeps you from grading memorization as if it were skill.

Justy Mm-hm.

Cody Then you run the plain setup on that test side. Same task, same scoring rule, no special trick. That score becomes the floor mark. After that, you apply the new method and run the same evaluation again. The gap is the part you actually get to argue about.

Justy And one of the common new methods is supervised fine-tuning. We did episode seven thirty-eight on that, but quick version: you take a pretrained model and shape its behavior with examples of the answers you want.

Cody Right, and if the claim is that your fine-tuning recipe helped, the baseline should usually be the same pretrained model without your recipe, or the same model trained the older way. If you compare your tuned model to a tiny random model, congratulations, you beat a chair.

Cody I mean, it sounds silly, but that mistake shows up in more polished clothing. A weak baseline makes the lift look huge. It turns a modest change into a fireworks chart. And then everyone has to waste time figuring out whether the idea worked or the comparison was just soft.

Justy This is where model generalization comes in too. Episode seven fifty, for the full thing. But the compact version is: generalization is whether the model handles new cases, not just the exact examples it saw during training.

Cody Yes. Baselines and generalization are tied together because both are anti-self-deception tools. If your new system beats the baseline only on examples it basically saw before, that is not the same as improving the real task. It might just be better at the homework packet.

Justy Sure.

Cody So in our floor-mark picture, the floor has to be measured in the same room where you are claiming the ceiling is higher. Same test setup. Same held-out cases. Same scoring rule. Otherwise you're comparing your fancy building to a different building across town and acting confused.

Justy Which is a very Exploring Next way to spend a Wednesday: defending the honor of the floor.

Justy But this is why I like baselines from a product angle. In production, the baseline is often not a cute academic toy. It's the thing people are already using. If the new system does not beat that, in a way that matters, nobody cares that the architecture has nicer vibes.

Cody Yes, and that changes the question. In research, a baseline proves your idea contributes something. In production, the baseline is the cost of switching. The old process may be ugly, but it is integrated, monitored, and understood. Your new model has to earn the disruption.

Justy Okay okay.

Cody This is why the phrase strong baseline matters. A weak baseline is random guessing on a task where any halfway reasonable model will crush it. A strong baseline is a serious existing approach. If your method beats that, even by a smaller amount, the claim is much more believable.

Justy But can a baseline be too strong? Because I hear that and my product brain goes, wait, isn't stronger always better for comparison?

Cody Not always. If the baseline is unfairly advantaged, it can hide real progress. Say it is a massive model trained on data that overlaps the test set, or a hand-tuned system with private features the new method is not allowed to use. Then you're not asking whether the idea helps. You're staging a weird cage match.

Justy So the baseline has to be strong enough to be honest, but not so loaded that it answers a different question. That's annoyingly subtle.

Cody It is. The baseline should match the claim. New training technique? Compare against the old training technique on the same model. New architecture? Compare against the prior architecture under similar conditions. New production assistant? Compare against the current workflow people actually rely on.

Justy That Backbase banking case is a good modern anchor. The headline was a twelve billion parameter banking model beating GPT-four point one in live deployment. The interesting baseline question is not just, did it beat a famous general model? It's, what was the deployed banking task, and what comparison mattered to the institution?

Cody Exactly. A domain model can beat a larger general model when the task, data, and evaluation are specific. But the comparison only teaches you something if the baseline is the thing you would plausibly choose instead. Otherwise the win is just a trophy on a shelf.

Justy Right.

Cody Same with avatarin using GPT-Realtime for retail support. They had thirty thousand shoppers in a two-week public experience and reported ninety two percent post-use something positive. That's useful operational evidence, but a baseline would ask: how did this compare with the old support channel, the old conversion path, or the old satisfaction measure?

Justy And BASF with AlphaEvolve managing thousands of supply-chain decisions has the same shape. The baseline is probably some current planning process, heuristic, or optimization setup. If you don't know that floor, thousands of decisions sounds impressive but floats in the air.

Cody Mm-hm.

Justy Also, thousands of supply-chain decisions is such a phrase. It sounds like a spreadsheet escaped the building, learned scheduling, and demanded a title.

Justy Sorry. Back to being extremely serious floor enthusiasts.

Cody No, that joke is basically the point. A lot of machine learning evaluation is making the escaped spreadsheet say what it replaced. A simple rule-based system can be a baseline. A previous published result can be a baseline. A pretrained model with no fine-tuning can be a baseline.

Justy This is also what that Why Powerful ML Is Deceptively Easy piece kept poking at. Sometimes the supposedly boring setup is already strong, and if your elaborate thing cannot beat it cleanly, the boring setup is not embarrassing. It's the bar.

Cody Yes. And that bar should be reported clearly. If a paper says our model got a score, but hides or muddies the baseline, I don't know what to do with the number. Good reporting says what the baseline was, how it was run, and whether the comparison is apples to apples.

Justy Here's where I think people get tripped up. They hear baseline and think disposable prototype. Like, you make it, beat it, throw it away. But in teams, the baseline often stays alive as the sanity check.

Cody Totally. You keep the baseline because future changes need the same reference point. If version three beats version two but version two was already worse than the original simple baseline on some cases, you want to know that. Otherwise progress can drift into elaborate regression.

Justy Where does this stand now, then? Because some Overview concepts are historical scaffolding. Baseline model feels… very not historical.

Cody Still core. No asterisk. The hard part has moved from knowing that you need a baseline to choosing a fair one for open-ended systems: chat, code agents, multimodal models, long workflows. With a binary classifier, a coin flip or majority class might be obvious. With an agent, the current human-assisted workflow may be the only baseline that matters.

Justy That lands for me. The baseline is not anti-innovation. It's what lets you say the innovation survived contact with reality. Especially in enterprise stuff, where the question is not, can it produce an impressive demo, but can it replace or improve the thing with receipts.

Cody And the one thing I would keep is this: a baseline model is the answer to compared to what. If you cannot answer that, you do not yet have evidence of improvement. You have a number looking for a story.

Justy Cody, I cannot believe it took episode ten fifty-eight for us to defend the floor this hard. Honestly… worth it.