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The Bitter Lesson Is Misunderstood

obviouslywrong.substack.com

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Re: The Bitter Lesson Is Misunderstood

#11

Post by mediaman »

FloorEgg wrote: post #2

The problem I am facing in my domain is that all of the data is human generated and riddled with human errors. I am not talking about typos in phone numbers, but rather fundamental errors in critical thinking, reasoning, semantic and pragmatic oversights, etc. all in long-form unstructured text. It's very much an LLM-domain problem, but converging on the existing data is like trying to converge on noise. The opportun…

This is one reason why verifiable rewards works really well, if it's possible for a given domain. Figuring out how to extract signal and verify it for an RL loop will be very popular for a lot of niche fields.

Re: The Bitter Lesson Is Misunderstood

#13

Post by FloorEgg »

stego-tech wrote: post #9
FloorEgg wrote: post #2

The problem I am facing in my domain is that all of the data is human generated and riddled with human errors. I am not talking about typos in phone numbers, but rather fundamental errors in critical thinking, reasoning, semantic and pragmatic oversights, etc. all in long-form unstructured text. It's very much an LLM-domain problem, but converging on the existing data is like trying to converge on noise. The opportun…

This is my current drum I bang on when an uninformed stakeholder tries shoving LLMs blindly down everyone’s throats: it’s the data, stupid . Current data aggregates outside of industries wholly dependent on it (so anyone not in web advertising, GIS, or intelligence) are garbage , riddled with errors and in awful structures that are opaque to LLMs. For your AI strategy to have any chance of success, your data has to b…

Yes, for me both customers and colleagues continually suggested "hey let's just take all these samples of past work and dump it in the magical black box and then replicate what they have been doing".

Instead I developed a UX that made it as easy as possible for people to explain what they want to be done, and a system that then goes and does that. Then we compare the system's output to their historical data and there is always variance, and when the customer inspects the variance they realize that their data was wrong and the system's output is far more accurate and precise than their process (and ~3 orders of magnitude cheaper). This is around when they ask how they can buy it.

This is the difference between making what people actually want and what they say they want: it's untangling the why from the how.

Re: The Bitter Lesson Is Misunderstood

#14

Post by NooneAtAll3 »

while I don't disagree with the facts, I don't understand the... tone?

when Dennard scaling (single core performance) started to fail in 90s-00s, I don't think there was a sentiment "how stupid was it to believe such a scaling at all"?

sure, people were compliant (and we still meme about running Crysis), but in the end the discussion resulted in "no more free lunch" - progress in one direction has hit a bottleneck, so it's time to choose some other direction to improve on (and multi-threading has now become mostly the norm)

I don't really see much of a difference?

Re: The Bitter Lesson Is Misunderstood

#15

Post by mannykannot »

pizzly wrote: post #8
jvanderbot wrote: post #5

Earlier quoted context omitted.

Play in the real world generates a data point every few minutes. Seems a bit slow?

Humans experience (play in the real world) is multi modal though vision, sound, touch, pressure, muscle feedback, gravitational, etc. Its extremely rich in data. Its also not a data point its continuous stream of information. Also I would bet that humans synthesize data at the same time. Everytime we run multiple scenarios in our mind before choosing the one we execute without even thinking about it is synthesizing d…

That's true, but still, a single individual or small group living isolated in the real world will, over a lifetime, learn only a tiny fraction of what we can learn from the written knowledge accumulated over millennia.

Having said that, I tend to agree that having AI interact with the world may be key: for one thing, I'm not sure whether there is any sense in which LLMs understand that most of the information content of language is about an external world.

Re: The Bitter Lesson Is Misunderstood

#16

Post by antirez »

cs702 wrote: post #3

I don't think Sutton's essay is misunderstood, but I agree with the OP's conclusion: We're reaching scaling limits with transformers. The number of parameters in our largest transformers, N, is now in the order of trillions, which is the most we can apply given the total number of tokens of training data available worldwide, D, also in the order of trillions, resulting in a compute budget C = 6N × D, which is in the…

This is true for the pre-training step. What if advancements in the reinforcement learning steps performed later may benefit from more compute and more models parameters? If right now the RL steps only help with sampling, that is, they only optimize the output of a given possible reply instead of the other (there are papers pointing at this: that if you generate many replies with just the common sampling methods, and you can verify correctness of the reply, then you discover that what RL helps with is selecting what was already potentially within the model output) this would be futile. But maybe advancements in the RL will do to LLMs what AlphaZero-alike models did with Chess/Go.

Re: The Bitter Lesson Is Misunderstood

#17

Post by charleshn »

cs702 wrote: post #3

I don't think Sutton's essay is misunderstood, but I agree with the OP's conclusion: We're reaching scaling limits with transformers. The number of parameters in our largest transformers, N, is now in the order of trillions, which is the most we can apply given the total number of tokens of training data available worldwide, D, also in the order of trillions, resulting in a compute budget C = 6N × D, which is in the…

> We cannot add more compute to a given compute budget C without increasing data D to maintain the relationship. > We must either (1) discover new architectures with different scaling laws, and/or (2) compute new synthetic data that can contribute to learning (akin to dreams).

Of course we can, this is a non issue.

See e.g. AlphaZero [0] that's 8 years old at this point, and any modern RL training using synthetic data, e.g. DeepSeek-R1-Zero [1].

[0] https://en.m.wikipedia.org/wiki/AlphaZero

[1] https://arxiv.org/abs/2501.12948

Re: The Bitter Lesson Is Misunderstood

#18

Post by airstrike »

FloorEgg wrote: post #10
tliltocatl wrote: post #7

Earlier quoted context omitted.

That's the endgame, but on the other hand, we already have one, it's called "humanity". No reason to believe that another one would be much cheaper. Interacting with the real world is __expensive__. It's the most expensive thing of all.

Very true. Living cells are ~4-5 orders of magnitude more functional-information-dense than the most advanced chips, and there is a lot more living mass than advanced chips. But the networking potential of digital compute is a fundamentally different paradigm than living systems. The human brain is constrained in size by the width of the female pelvis. So while it's expensive, we can trade scope-constrained robustnes…

> Living cells are ~4-5 orders of magnitude more functional-information-dense than the most advanced chips, and there is a lot more living mass than advanced chips.

I believe you but I would love to know where this number came from just so I can read more about it

Re: The Bitter Lesson Is Misunderstood

#19

Post by __d »

FloorEgg wrote: post #10
tliltocatl wrote: post #7

Earlier quoted context omitted.

That's the endgame, but on the other hand, we already have one, it's called "humanity". No reason to believe that another one would be much cheaper. Interacting with the real world is __expensive__. It's the most expensive thing of all.

Very true. Living cells are ~4-5 orders of magnitude more functional-information-dense than the most advanced chips, and there is a lot more living mass than advanced chips. But the networking potential of digital compute is a fundamentally different paradigm than living systems. The human brain is constrained in size by the width of the female pelvis. So while it's expensive, we can trade scope-constrained robustnes…

> The human brain is constrained in size by the width of the female pelvis.

Well, it _was_ until recently.

Re: The Bitter Lesson Is Misunderstood

#20

Post by jeremyjh »

charleshn wrote: post #17
cs702 wrote: post #3

I don't think Sutton's essay is misunderstood, but I agree with the OP's conclusion: We're reaching scaling limits with transformers. The number of parameters in our largest transformers, N, is now in the order of trillions, which is the most we can apply given the total number of tokens of training data available worldwide, D, also in the order of trillions, resulting in a compute budget C = 6N × D, which is in the…

> We cannot add more compute to a given compute budget C without increasing data D to maintain the relationship. > We must either (1) discover new architectures with different scaling laws, and/or (2) compute new synthetic data that can contribute to learning (akin to dreams). Of course we can, this is a non issue. See e.g. AlphaZero [0] that's 8 years old at this point, and any modern RL training using synthetic dat…

AlphaZero trained itself through chess games that it played with itself. Chess positions have something very close to an objective truth about the evaluation, the rules are clear and bounded. Winning is measurable. How do you achieve this for a language model?

Yes, distillation is a thing but that is more about compression and filtering. Distillation does not produce new data in the same way that chess games produce new positions.

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