Leon Lang's Shortform

Leon Lang

This is a special post for quick takes by Leon Lang. Only they can create top-level comments. Comments here also appear on the Quick Takes page and All Posts page.

https://x.com/sama/status/1813984927622549881

According to Sam Altman, GPT-4o mini is much better than text-davinci-003 was in 2022, but 100 times cheaper. In general, we see increasing competition to produce smaller-sized models with great performance (e.g., Claude Haiku and Sonnet, Gemini 1.5 Flash and Pro, maybe even the full-sized GPT-4o itself). I think this trend is worth discussing. Some comments (mostly just quick takes) and questions I'd like to have answers to:

Should we expect this trend to continue? How much efficiency gains are still possible? Can we expect another 100x efficiency gain in the coming years? Andrej Karpathy expects that we might see a GPT-2 sized "smart" model.
What's the technical driver behind these advancements? Andrej Karpathy thinks it is based on synthetic data: Larger models curate new, better training data for the next generation of small models. Might there also be architectural changes? Inference tricks? Which of these advancements can continue?
Why are companies pushing into small models? I think in hindsight, this seems easy to answer, but I'm curious what others think: If you have a GPT-4 level model that is much, much cheaper, then you can sell the service to many more people and deeply integrate your model into lots of software on phones, computers, etc. I think this has many desirable effects for AI developers:
- Increase revenue, motivating investments into the next generation of LLMs
- Increase market-share. Some integrations are probably "sticky" such that if you're first, you secure revenue for a long time.
- Make many people "aware" of potential usecases of even smarter AI so that they're motivated to sign up for the next generation of more expensive AI.
- The company's inference compute is probably limited (especially for OpenAI, as the market leader) and not many people are convinced to pay a large amount for very intelligent models, meaning that all these reasons beat reasons to publish larger models instead or even additionally.
What does all this mean for the next generation of large models?
- Should we expect that efficiency gains in small models translate into efficiency gains in large models, such that a future model with the cost of text-davinci-003 is massively more capable than today's SOTA? If Andrej Karpathy is right that the small model's capabilities come from synthetic data generated by larger, smart models, then it's unclear to me whether one can train SOTA models with these techniques, as this might require an even larger model to already exist.
- At what point does it become worthwhile for e.g. OpenAI to publish a next-gen model? Presumably, I'd guess you can still do a lot of "penetration of small model usecases" in the next 1-2 years, leading to massive revenue increases without necessarily releasing a next-gen model.
- Do the strategies differ for different companies? OpenAI is the clear market leader, so possibly they can penetrate the market further without first making a "bigger name for themselves". In contrast, I could imagine that for a company like Anthropic, it's much more important to get out a clear SOTA model that impresses people and makes them aware of Claude. I thus currently (weakly) expect Anthropic to more strongly push in the direction of SOTA than OpenAI.

2Leon Lang2y

One question: Do you think Chinchilla scaling laws are still correct today, or are they not? I would assume these scaling laws depend on the data set used in training, so that if OpenAI found/created a better data set, this might change scaling laws. Do you agree with this, or do you think it's false?

New data! Llama 3 report includes data about Chinchilla optimality study on their setup. The surprise is that Llama 3 405b was chosen to have the optimal size rather than being 2x overtrained. Their actual extrapolation for an optimal point is 402b parameters, 16.55T tokens, and 3.8e25 FLOPs.

Fitting to the tokens per parameter framing, this gives the ratio of 41 (not 20) around the scale of 4e25 FLOPs. More importantly, their fitted dependence of optimal number of tokens on compute has exponent 0.53, compared to 0.51 from the Chinchilla paper (which was al... (read more)

4Vladimir_Nesov2y

Data varies in the loss it enables, doesn't seem to vary greatly in the ratio between the number of tokens and the number of parameters that extracts the best loss out of training with given compute. That is, I'm usually keeping this question in mind, didn't see evidence to the contrary in the papers, but relevant measurements are very rarely reported, even in model series training report papers where the ablations were probably actually done. So could be very wrong, generalization from 2.5 examples. With repetition, there's this gradual increase from 20 to 60. Probably something similar is there for distillation (in the opposite direction), but I'm not aware of papers that measure this, so also could be wrong. One interesting point is the isoFLOP plots in the StripedHyena post (search "Perplexity scaling analysis"). With hybridization where standard attention remains in 8-50% of the blocks, perplexity is quite insensitive to change in model size while keeping compute fixed, while for pure standard attention the penalty for deviating from the optimal ratio to a similar extent is much greater. This suggests that one way out for overtrained models might be hybridization with these attention alternatives. That is, loss for an overtrained model might be closer to Chinchilla optimal loss with a hybrid model than it would be for a similarly overtrained pure standard attention model. Out of the big labs, visible moves in this directions were made by DeepMind with their Griffin Team (Griffin paper, RecurrentGemma). So that's one way the data wall might get pushed a little further for the overtrained models.