All of Tao Lin's Comments + Replies

Leela Zero uses MCTS, it doesnt play superhuman in one forward pass (like gpt-4 can do in some subdomains) (i think, didnt find any evaluations of Leela Zero at 1 forward pass), and i'd guess that the network itself doesnt contain any more generalized game playing circuitry than an llm, it just has good intuitions for Go.



Subjectively there is clear improvement between 7b vs. 70b vs. GPT-4, each step 1.5-2 OOMs of training compute.

1.5 to 2 OOMs? 7b to 70b is 1 OOM of compute, adding in chinchilla efficiency would make it like 1.5 OOMs of effective... (read more)

2Vladimir Nesov4mo
Good catch, since the context from LLMs is performance in one forward pass, the claim should be about that, and I'm not sure it's superhuman without MCTS. I think the intended point survives this mistake, that is it's a much smaller model than modern LLMs that has relatively very impressive performance primarily because of high quality of the synthetic dataset it effectively trains on. Thus models at the scale of near future LLMs will have a reality-warping amount of dataset quality overhang. This makes ability of LLMs to improve datasets much more impactful than their competence at other tasks, hence the anchors of capability I was pointing out were about labeling and rearranging data according to instructions. And also makes compute threshold gated regulation potentially toothless. With Chinchilla scaling, compute is square of model size, so 2 OOMs under that assumption. Of course current 7b models are overtrained compared to Chinchilla (all sizes of LLaMA-2 are trained on 2T tokens), which might be your point. And Mistral-7b is less obviously a whole step below LLaMA-2-70b, so the full-step-of-improvement should be about earlier 7b models more representative of how the frontier of scaling advances, where a Chinchilla-like tradeoff won't yet completely break down, probably preserving data squared compute scaling estimate (parameter count no longer works very well as an anchor with all the MoE and sparse pre-training stuff). Not clear what assumptions make it 1.5 OOMs instead of either 1 or 2, possibly Chinchilla-inefficiency of overtraining? I was going from EpochAI estimate that puts LLaMA 2 at 8e23 FLOPs and GPT-4 at 2e25 FLOPs, which is 1.4 OOMs. I'm thinking of effective compute in terms of compute necessary for achieving the same pre-training loss (using lower amount of literal compute with pre-training algorithmic improvement), not in terms of meaningful benchmarks for fine-tunes. In this sense overtrained smaller LLaMAs get even less effective compute tha
2Buck Shlegeris4mo
Iirc, original alphago had a policy network that was grandmaster level but not superhuman without MCTS.

If automating alignment research worked, but took 1000+ tokens per researcher-second, it would be much more difficult to develop, because you'd need to run the system for 50k-1M tokens between each "reward signal or such". Once it's 10 or less tokens per researcher second, it'll be easy to develop and improve quickly.

I'm not against evaluating models in ways where they're worse than rocks, I just think you shouldn't expect anyone else to care about your worse-than-rock numbers without very extensive justification (including adversarial stuff)

Externalized reasoning models suffer from the "legibility penalty" - the fact that many decisions are easier to make than to justify or explain. I think this is a significant barrier for authentic train of thought competitiveness, although not for particularly legible domains, such as math proofs and programming (Illegible knowledge goes into math proofs, but you trust the result regardless so it's fine).

Another problem is that standard training procedures only incentivize the model to use reasoning steps produced by a single human. This means, for instanc... (read more)