The Ledger
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Future LLMs will prioritize architectural efficiency over larger model sizes
“I wouldn't expect bigger architectures. I would expect a more efficient architectures tweaks getting, The same modeling performance for less compute”
Process Reward Models will eventually become standard in LLM post-training
“I think it is promising and we will see it working at some point. I think it's just like right now it's still Tricky to make it work, but I am quite sure we'll see it as part of the standard repertoire at some point.”
Large enterprises are secretly hiring teams to train ChatGPT-scale LLMs in-house
“I know for a fact that big companies are training now LLMs in-house. Really, like, big companies who have the financial means to train chat to be like model are hiring people who train LLMs.”
Transformer remains state of the art for LLM performance
“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.”
DeepSeek's architecture is still built on a GPT-2 scaffold
“And you can actually, in fact, Take a GPT one or two model and with a few, I mean, few lines of code almost, you can transform it into the latest let's say deep seek version, 3.2 architecture. It's not like a big leap. It's still the same as scaffold.”
Major AI firm will launch a frontier text diffusion model in 2026
“I think one company will launch a big diffusion model this year.”
50 RLVR steps boosted Qwen 3 MATH-500 score from 15% to 50%
“I took the Quinn three model as part of my book, the reasoning from scratch book. And I trained it just for 50 steps with RLVR, and it goes from 15%, so one five percent accuracy on math 500 to 50% on math 500.”
DeepSeek-R1 cost roughly $300,000 to train, 10x cheaper than DeepSeek-V3
“Deep seek version three, they had like a five million dollar price tag on that given the, I think two dollars per GPU, they assume whether that's a correct assumption or not it's a different question, but if you compare it relative to the cost of R one, I thin…”
AI evaluation will shift from single-shot answers to multi-step agentic execution
“Maybe it's not the one shot problem anymore where it's not really answering knowledge question. That's not really solving math problems in, in one iteration of the benchmark. It is maybe more like the agentic cycle, like where you have like a more like a objec…”
Self-improving AI and continual learning agents will not be feasible in 2026
“If you have an LLM that self improves or like an agent that does something fails and learns, I don't think anything like that is feasible this year.”
Predicting internal states might push state-of-the-art for code LLMs
“And I do think that is something that is maybe more expensive to do, but it is also something that might push the state of the art a little bit.”
RLVR and GRPO will stabilize into canonical algorithms similar to AdamW
“It's similar to, you know, optimizers with Adam. So Adam is, I mean, right now there's Adam W. There was SGD and all the other RMS prop and how they were called. And they kind of all converge to Adam W by adding more and more tricks. And I think that's the sam…”
The AI industry is rapidly running out of challenging evaluation benchmarks
“The only thing is we are running out of is really benchmarks. So the improvement on benchmarks, it's kind of like harder to measure.”
GPT OSS benchmarks demonstrate 1.2x capability jump when tool calling is enabled
“And also you can actually go to the GPT OSS release block, and they did have benchmarks to show how the performance on the benchmarks is with the same model with tool called enabled and disabled. And you can actually see there is, I mean, it's not like two tim…”
Hierarchical Reasoning Model matches larger LLMs on ARC using Transformer architecture
“Hierarchical reasoning model, it became like popular because it performed relatively well on that benchmark compared to very expensive models like Gemini, Chachupiti, and so forth. And it is a transformer architecture.”