Maxime Riché

Wiki Contributions


Right 👍

So the effects are:

Effects that should increase Anthropic's salaries relative to OpenAI: A) - The pool of AI safety focused candidates is smaller B) - AI safety focused candidates are more motivated

Effects that should decrease Anthropic's salaries relative to OpenAI: C) - AI safety focused candidates should be willing to accept significantly lower wages

New notes: (B) and (C) could cancel each other but that would be a bit suspicious. Still a partial cancellation would make a difference between OpenAI and Anthropic lower and harder to properly observe. (B) May have a small effect, given that hires are already world level talents, it would be weird that they could significantly increase even more their performance by simply being more motivated. I.e. non AI safety focused candidates are also very motivated. The difference in motivation between both groups is possibly not large.

These forecasts are about the order under which functionalities see a jump in their generalization (how far OOD they work well).

By "Generalisable xxx" I meant the form of the functionality xxx that generalize far.

Rambling about Forecasting the order in which functions are learned by NN

Using function complexity and their "compoundness", we may be able to forecast the order in which algorithms in NN are learned. And we may be able to forecast the temporal ordering of when some functions or behaviours will start generalising strongly.

What happens when training neural networks is similar to the selection of genes in genomes or any reinforcement optimization processes. Compound functions are much harder to learn. You need each part to be independently useful initially to provide enough signal for the compound system to be reinforced. 

That means that learning any non-hardcoded algorithms with many variables and multiplicative steps is very difficult. 
An important factor in this is the frequency at which an algorithm is useful and to which extent.  An algorithm that can be very used in most situations will get much more training signals. The relative strength of the reward signal you get is important because of the noise in the training and because of catastrophic forgetting. 

LLMs are not learning complex algorithms yet. They are learning something like a world model because this is used for most tasks and it can be built by first building each part separately and then assembling them. 

Regarding building algorithms to exploit this world model, it can be learned later if the algorithm is composed first of very simple algorithms that can be later assembled. An extra difficulty for LLMs to learn algorithms is in situations where heuristics already work very well. In that case, you need to add significant regularisation pushing for simpler circuits. Then you may observe grokking and a transition from heuristics to algorithms.

An issue with this reasoning is that heuristics are 1-step algorithms (0 compoundness).


- Frequency of reward

- Strength of the additional reward (above the "heuristic baseline")

- Compoundness

Forecasting game:
(WIP, mostly a failure at that point)

Early to generalize well:
World models can be built from simple parts, and are most of the time valuable. 
Generalizable algorithm for simple and frequent tasks on which heuristics fail dramatically: ??? (maybe) generating random numbers, ??

Medium to generalize well:
Generalizable deceptive alignment algorithms: They require several components to work. But they are useful for many tasks. The strength of the additional reward is not especially high or low.
Generalizable instrumental convergence algorithms: Same as deceptive alignment.
Generalizable short horizon algorithms: They, by definition, require fewer sequential steps, as such they should be less "compounded" functions and appear sooner.

Generalizable long horizon algorithms: They, by definition, require more sequential steps, as such they should be more "compounded" functions and appear later.

The latest:
Generalizable long horizon narrow capabilities: They are not frequently reinforced. 

(Time spent on this: 45min)


July 6th update: 
Here is a quick experiment trying to observe the effect of increasing "compoundness" on the ordering of grokking different functions:

Quick results:
The task is predicting the sign of the product of 1 (function 1) to 8 (function 8) standard normal random variables. 
Increasing the compoundness by 2 seems to delay the grokking by something like 1 OOM.

Will we get to GPT-5 and GPT-6 soon?

This is a straightforward "follow the trend" model which tries to forecast when GPT-N-equivalent models will be first trained and deployed up to 2030.

Baseline forecast: 

 GPT-4.7 GPT-5.3GPT-5.8GPT-6.3
Start of training2024.42025.52026.52028.5

Bullish forecast:

Start of training2024.420252026.52028.5

FWIW,  it predicts roughly similar growth in model size, energy cost and GPU count than described in while being created the week before this was released.

I spent like 10 hours on this, so I expect to find lingering mistakes in the model.

Could Anthropic face an OpenAI drama 2.0?

I forecast that Anthropic would likely face a similar backlash from its employees than OpenAI in case Anthropic’s executives were to knowingly decrease the value of Anthropic shares significantly. E.g. if they were to switch from “scaling as fast as possible” to “safety-constrained scaling”. In that case, I would not find it surprising that a significant fraction of Anthropic’s staff threatened to leave or leave the company.

The reasoning is simple, given that we don’t observe significant differences in the wages of OpenAI and Anthropic employees and assuming that they are overall of the same distribution of skill and skill level. Then it seems that Anthropic is not able to use the argument of its AI safety focus as a bargaining argument to reduce the wages significantly. If true this would mean that safety is of relatively little importance to most of Anthropic’s employees.

Counter argument: Anthropic is hiring from a much more restricted pool of candidates. From only the safety-concerned candidates. In that case, Anthropic would have to pay a premium to hire these people. And it happens that this premium is roughly equivalent to the discount that these employees are willing to give to Anthropic because of its safety focus.

The words evaluations, experiments, characterizations, and observations are somewhat confused or confusingly used in discussions about model evaluations (e.g., refref). 

Let’s define them more clearly: 

  • Observations provide information about an object (including systems).
    • This information can be informative (allowing the observer to update its beliefs significantly), or not.
  • Characterizations describe distinctive features of an object (including properties).
    • Characterizations are observations that are actively designed and controlled to study an object.
  • Evaluations evaluate the quality of distinctive features based on normative criteria.
    • Evaluations are composed of both characterizations and normative criteria.
    • Evaluations are normative, they inform about what is good or bad, desirable or undesirable.
    • Normative criteria (or “evaluation criterion”) are the element bringing the normativity. They are most of the time directional or simple thresholds.
    • Evaluations include both characterizations of the object studied and characterization of the characterization technique used (e.g., accuracy of measurement).
  • Scientific experiments test hypotheses through controlled manipulation of variables.
    • Scientific experiments are composed of: characterizations, and hypothesis

In summary:

  • Observations 
  • Characterizations = Designed and controlled Observations
  • EvaluationsCharacterization of object + Characterization of the characterization method + Normative criteria
  • Scientific experimentsCharacterizations + Hypothesis


  • An observation is an event in which the observer receives information about the AI system.
    • E.g., you read a completion returned by a model.
  • A characterization is a tool or process used to describe an AI system.
    • E.g., you can characterize the latency of an AI system by measuring it. You can characterize how often a model is correct (without specifying that correctness is the goal). 
  • An AI system evaluation will associate characterizations and normative criteria to conclude about the quality of the AI system on the dimensions evaluated.
    • E.g., alignment evaluations use characterizations of models and the normative criteria of the alignment with X (e.g., humanity) to conclude on how well the model is aligned with X.
  • An experiment will associate hypotheses, interventions, and finally characterizations to conclude on the veracity of the hypotheses about the AI system.
    • E.g., you can change the training algorithm and measure the impact using characterization techniques.

Clash of usage and definition:

These definitions slightly clash with the usage of the term evals or evaluations in the AI community. Regularly the normative criteria associated with an evaluation are not explicitly defined, and the focus is solely put on the characterizations included in the evaluation.

(Produced as part of the AI Safety Camp, within the project: Evaluating alignment evaluations)


Interestingly, after a certain layer, the first principle component becomes identical to the mean difference between harmful and harmless activations.


Do you think this can be interpreted as the model having its focus entirely on "refusing to answer" from layer 15 onwards? And if it can be interpreted as the model not evaluating other potential moves/choices coherently over these layers. The idea is that it could be evaluating other moves in a single layer (after layer 15) but not over several layers since the residual stream is not updated significantly. 

Especially can we interpret that as the model not thinking coherently over several layers about other policies, it could choose (e.g., deceptive policies like defecting from the policy of "refusing to answer")? I wonder if we would observe something different if the model was trained to defect from this policy conditional on some hard-to-predict trigger (e.g. whether the model is in training or deployment).

Thank for the great comment!

Do we know if distributed training is expected to scale well to GPT-6 size models (100 trillions parameters) trained over like 20 data centers? How does the communication cost scale with the size of the model and the number of data centers? Linearly on both?

After reading for 3 min this:
Google Cloud demonstrates the world’s largest distributed training job for large language models across 50000+ TPU v5e chips (Google November 2023). It seems that scaling is working efficiently at least up to 50k GPUs (GPT-6 would be like 2.5M GPUs). There are also some surprising linear increases in start time with the number of GPUs, 13min for 32k GPUs. What is the SOTA?

The title is clearly an overstatement. It expresses more that I updated in that direction, than that I am confident in it. 

Also, since learning from other comments that decentralized learning is likely solved, I am now even less confident in the claim, like only 15% chance that it will happen in the strong form stated in the post.

Maybe I should edit the post to make it even more clear that the claim is retracted.

Load More