Comment by ux266478 | Hacker News Reader

Comment by ux266478 | Hacker News Reader

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This tracks with my experience. A core and integral part to make models actually shine involves post training and custom harness engineering, all specifically done for the purpose of getting them to settle into competent inputs and outputs that are relevant to you, optimized for the harness you build which better suits your domain. The weights are just a generalization. A block of marble to be sculpted.

As the compute to build adaptions for multi-billion to trillion parameter models becomes more and more available (and affordable), and the artistic techniques of fine tuning and harness engineering spread throughout the public, I think we'll come to see the "one size fits all" model for the non-starter it really is. Anybody who's even toyed around with LoRAs (let alone made their own) already knows this. That's a very deep rabbit hole, and the ceiling is determined by target domain knowledge and systems engineering.

I'm bullish on LLMs as expert tools in the same grain that computers are. You have to learn all about them to use them effectively. But what really makes the difference is how well you know what you're going to be pointing them at. There is very little room for companies like OpenAI or Google to live between us and our tools.

Sorry not an AI specialist, what exactly do you mean by 'custom harness engineering'? Some way of informing the model whether the output it is producing is good or bad based on the specific task in question?

Harnesses are the control surface the model interacts with. How it invokes tools, the tools it has access to, how agents are coordinated. It's like an interface or a shell. It's the magic that lets an LLM operate a computer. You can read more here: https://learn.microsoft.com/en-us/agent-framework/concepts/h...

> Some way of informing the model whether the output it is producing is good or bad

That's what post-training is for. At its most basic, you're giving it examples of inputs and outputs and then doing reinforcement learning to calibrate its adaptation to your examples. You need much less to fine tune a model than you do to pretrain the weights. You can build a really excellent LoRA for a stable diffusion model, for example, with 50 high quality images. LLMs are "a bit" more complicated and costly to fine tune, and you have to be mindful of the agentic loop, but the principle is the same. There's more to it than just LoRAs. Steering vectors, projection layers, custom encoders, etc. There's a fair amount to learn, but it sounds a lot scarier than it is.

Here's something to chew on: chain-of-thought doesn't exist until after pretraining! It's basically created by having <think>...</think> blocks directly in some example outputs, and this is fine-tuned into stability. It's literally not much more than a parlor trick and some careful calibration. A powerful parlor trick to be sure, though.

Aren't you just betting against the bitter lesson though?

Given that we have an embodied intelligence that is capable of being reasonably good at physics, it would be foolish to state categorically that AI can't do physics. I am however skeptical that LLMs can do physics. My experience is that it's really great at doing the stuff I can't be arsed to do, and is therefore very useful, but it has very poor "understanding" of physics

No, that would be a fundamental misunderstanding of the bitter lesson, which is about research bets over time. The object of comparison is the technology, the underlying substrate and fundamental architecture, and how much of it can be offloaded to computation. We're talking about the same architecture here, they're both transformers. The difference doesn't exist in a relevant way to the question.

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