State of LLMs 2026: RLVR, GRPO, Inference Scaling — Sebastian Raschka
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What is the main topic discussed in this episode?
Pre training is not dead, but pre training is boring. It's not where the low-hanging fruit is anymore. Improvement is not so much coming from the architecture anymore. It is basically the post training. I think one of the biggest drivers this year has been the entrance scaling. It goes from 1.5% accuracy to 50% by only doing 50 reinforcement learning steps. There's no one thing that fixes it all. It's a lot of little tips and tricks all over the place. If you add them up, that will give you the progress. But there's no magic bullet that gives you
Hi, I'm Matt Turk from Firstmark. Welcome to the Matt Podcast. Today my guest is Sebastian Rashke, an AI researcher and one of the best educators in the field, well known for his in-depth technical blog posts and his book entitled Build a Large Language Model from Scratch. In this episode, we go deep on the state of LLMs in 2026, architectures, post training, scaling, benchmarks, tool use, and what it all means for the next wave of AI. Please enjoy this.
What is the current status of transformer architectures and are they still the dominant design?
Hey Sebastian, welcome.
Thanks for inviting me on your podcast today. I'm excited to talk about anything AI, I guess.
Wonderful. So we are going to go uh in the state of LLMs in 2026 in depth, including very much uh post-training and reinforcement learning. But I wanted to start the conversation with the transformer architecture itself, uh, obviously the backbone of the entire generative AI revolution, uh, but also over eight years old at this point, and um for all the tremendous progress in LLM-based systems. systems over the last year. Uh it also seems that there have been some interesting developments uh in terms of alternative architectures. So uh has anything caught your eye and uh do you think that's a world where the days of the transformer architecture could finally be numbered?
Yeah, that is actually a very interesting uh question to start with. I mean it's starting at the very beginning with the transformer architecture. You said eight years. I think it's almost eight uh nine years because two thousand twenty six it came out in two thousand seventeen. Quite a long time. And I think the question you raised is if it's like, you know, the final architecture. I think people probably ask that every year. Is this the thing we should be betting on going forward, let's say in two thousand twenty six? I would say Right now, yes, because it's still the state of the art. So there is nothing really better in terms of state-of-the-art performance, getting better quality results. What we have seen so far though is alternatives that make it cheaper.
So they have tricks to make the architecture cheaper itself, like uh linear attention variants that are like a building block in the transformer architecture. A big one was a mixture of experts, with which is essentially making the model bigger with. without necess necessarily making it more expensive to use in inference, like keeping like that reasonable while expanding the size. You see all kinds of you I would say like levers, tips and tricks, hacks around that architecture, but it's still kind of like the same architecture in the core. And y you can actually in fact take a GPT one or two model and with a few, I mean a few lines of code almost, You can transform it into the latest uh let's say Deep Seek version 3.2 architecture.
It's not like a big leap, it's still the same uh scaffold. At the same time, you have other alternatives popping up, like you know, diffusion models, uh text diffusion uh particular, or Mamba models, state space models, and so forth. They all try to address a problem that the transformer has, namely that it is expensive and big and yeah. Expensive to run and train. But then of course there is no free lunch. These have other trade-offs. They are like cheaper to run in certain instances. If you take a look at diffusion models or text diffusion models, but then you don't get the same, let's say, quality out of it. And if you want to get the same quality out of it, in in this particular case, you have to crank up the denoising steps and then you end up with something very expensive.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–0:59
2
What is the current status of transformer architectures and are they still the dominant design?
0:59–6:00
3
What are small recursive (ARC) reasoning models and how do they achieve strong performance on logic puzzles?
6:00–10:01
4
How do text diffusion models work and can they replace auto‑regressive transformers?
10:01–14:27
5
How do world models differ from traditional LLMs and why are they gaining attention?
14:27–19:36
6
What is RLVR (reinforcement learning with verifiable rewards) and how does it pair with GRPO to improve reasoning?
19:36–44:01
7
Why is inference‑time scaling becoming a major driver of LLM performance in 2025‑2026?
44:01–48:54
8
How does tool‑calling reduce hallucinations and expand the capabilities of LLMs?
48:54–1:08:13
Speakers
1 identifiedMore from The MAD Podcast with Matt Turck
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