Forget LLMs. LRMs Are All The Rage Now

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GFlowNets: A Technical Deep Dive to Bridge the Worlds of Bayesian Inference and LLMs, The Alleged Secret to OpenAI’s o1 Success (for those that want to dive deeper into the technique, which is described below).

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Forget LLMs. LRMs Are All The Rage Now

Inevitable. The AI industry has shifted its focus to post-training techniques, transitioning the ubiquitous word ‘LLM’ in everyone’s mouth to ‘LRM,’ (standing for Large Reasoner Model), which are all the rage.

Even Microsoft’s CEO is now pumping his chest with the new law in town, the “inference-time compute” law, indirectly acknowledging the pre-training plateau we discussed last week and welcoming the ‘LRM era.’

Nevertheless, over the last few months, but especially the last two weeks, there has been an explosion of new releases from OpenAI, Alibaba, DeepSeek, Nous Research, MIT, Writer, Fireworks, and others. All offer the same ‘new thing’: they are all LRMs.

Today, we are deep-diving into these models, explaining all the different ways they are being built, from inference-time scaling to inference-time retrieval update, test-time training, and even GFlowNets, which seem to be the secret sauce that OpenAI is fiercely trying to protect.

By the end of this article, you will understand:

AI’s inescapable current limits incumbents and media don’t want you to know,

Why LLMs are no longer the name of the game,

2025’s Post-Training Framework: three ways to how AI labs are creating these exciting new AI models,

The emergence of a new training paradigm, GFlowNets, that will force you to reframe your understanding of AI completely,

Finally, we will define the perfect model to build in 2025 based on all the techniques described today, i.e., what will o2 or Claude 4 look like?

Let’s dive in.

LLMs Aren’t No Longer ‘It’

As we saw last week, scaling laws are plateauing. Brainlessly increasing model and dataset sizes no longer yields the outsized improvements they once did.

The Bitter Lesson

It’s quite funny when you look back, but not long ago, it seemed like the ‘AGI problem’ was solved, and the only thing we had to do was to unlock more compute.

The AI conversations were little about the algorithms and the technology and all about building GigaWatt data centers and buying as many GPUs as one possibly could. AI was less about science and more about religious beliefs. Life was simple.

But that’s no more.

Now, AI labs are desperate to create new ways to develop next-generation models. And most efforts involve techniques that take the LLM after pre-training and perform a series of methods to improve its performance, changing its natural behavior.

The Importance of Fine-tuning

Developing a frontier AI model involves more than pressing the training button and releasing the model once it's finished. After this training, the resulting model has compressed the required knowledge but doesn’t know how to elicit it.

Thus, it undergoes two significant next steps:

Supervised Fine-tuning (SFT): A phase where we send the model…

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