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We can see from the ablations that simply asking the LLM to generate a large number of potential solutions without the evolutionary search performs terribly. I

by Imnimo 3y ago

We can see from the ablations that simply asking the LLM to generate a large number of potential solutions without the evolutionary search performs terribly. It's certainly true that the LLM mutator produces more reasonable edits than a random mutator. But the value the LLM is bringing is that it knows how to produce reasonable-looking programs, not that it has some special understanding of bin packing or capset finding. The only thing the LLM sees is two previously generated code samples, labeled <function>_v0 and <function>_v1 and then a header for <function>_v2 which it fills in. Look at Extended Data Fig. 1.

famouswaffles 3y ago
With the current competency or state of the models today, both are necessary, nobody is denying that. It doesn't take a genius however to see who the more valuable contributor to the system is. The authors say as much when they anticipate much of any improvement to come from capabilities of better models than any change to the search algorithm. Gpt-4 may well have reduced the search time by half and so on. >But the value the LLM is bringing is that it knows how to produce reasonable-looking programs, not that it has some special understanding of bin packing or capset finding. So it is a general problem solver then, great. That's what we want.
- Imnimo 3y ago
  >It doesn't take a genius however to see who the more valuable contributor to the system is. Indeed, we can look at the ablations and see that swapping out a weaker code LLM (540B Codey to 15B Starcoder) makes little difference: "This illustrates that FunSearch is robust to the choice of the model as long as it has been trained sufficiently well to generate code." >So it is a general problem solver then, great. That's what we want. In the sense that it does not rely on the LLM having any understanding of the problem, yes. But not in the sense that it can be applied to problems that are not naturally decomposable into a single short Python function.
  - famouswaffles 3y ago
    >Indeed, we can look at the ablations and see that swapping out a weaker code LLM (540B Codey to 15B Starcoder) makes little difference Really stretching the meaning of "little difference" here "While ‘StarCoder’ is not able to find the full-sized admissible set in any of the five runs, it still finds large admissible sets that improve upon the previous state of the art lower bound on the cap set capacity." https://imgur.com/a/xerOOLn https://imgur.com/a/xerOOLn Literally telling you how much more valuable the LLM (any coding LLM) is than alternatives, while demonstrating prediction competence still matters.
    - Imnimo 3y ago
      I don't think five runs is sufficient to reach the conclusion you're reaching here, especially when we can see that one of the Codey runs is worse than all of the StarCoder runs. If Codey is so much more valuable, why is this happening? It's certainly true that LLM's are more valuable than the alternative tested - which is a set of hand-crafted code mutation rules. But it's important to think about why an LLM is better. There are two big pitfalls for the hand-crafted rules. First, the hand-crafted rule has to make local changes - it's vanishingly improbable to be able to do something like "change < to <= in each of this series of if-statements" or "increase all constants by 1", whereas those are natural simple changes that an LLM might make. The second is that the random mutations have no concept of parsimonious code. There's nothing preventing it from generating code that does stuff like compute values and then neglect to return them, or multiply a previous computation by zero, or any number of other obviously useless variations. What the LLM brings to the table here is the ability to avoid pitfalls like the above. It writes code that is shaped sensibly - it can make a more natural set of edits than "just randomly delete an operator". But that's all it's doing. That's not, like the tweet quoted above called it, "generating genuinely new knowledge". Put it this way - assuming for a moment that Codey ending up with a higher score than StarCoder is not random chance, do you think it's because Codey has some greater understanding of the admissible-set problem, or because Codey generates a different set of minor code edits?

by Imnimo 3y ago

famouswaffles 3y ago

With the current competency or state of the models today, both are necessary, nobody is denying that. It doesn't take a genius however to see who the more valuable contributor to the system is. The authors say as much when they anticipate much of any improvement to come from capabilities of better models than any change to the search algorithm. Gpt-4 may well have reduced the search time by half and so on. >But the value the LLM is bringing is that it knows how to produce reasonable-looking programs, not that it has some special understanding of bin packing or capset finding. So it is a general problem solver then, great. That's what we want.

Imnimo 3y ago
>It doesn't take a genius however to see who the more valuable contributor to the system is. Indeed, we can look at the ablations and see that swapping out a weaker code LLM (540B Codey to 15B Starcoder) makes little difference: "This illustrates that FunSearch is robust to the choice of the model as long as it has been trained sufficiently well to generate code." >So it is a general problem solver then, great. That's what we want. In the sense that it does not rely on the LLM having any understanding of the problem, yes. But not in the sense that it can be applied to problems that are not naturally decomposable into a single short Python function.
- famouswaffles 3y ago
  >Indeed, we can look at the ablations and see that swapping out a weaker code LLM (540B Codey to 15B Starcoder) makes little difference Really stretching the meaning of "little difference" here "While ‘StarCoder’ is not able to find the full-sized admissible set in any of the five runs, it still finds large admissible sets that improve upon the previous state of the art lower bound on the cap set capacity." https://imgur.com/a/xerOOLn https://imgur.com/a/xerOOLn Literally telling you how much more valuable the LLM (any coding LLM) is than alternatives, while demonstrating prediction competence still matters.
  - Imnimo 3y ago
    I don't think five runs is sufficient to reach the conclusion you're reaching here, especially when we can see that one of the Codey runs is worse than all of the StarCoder runs. If Codey is so much more valuable, why is this happening? It's certainly true that LLM's are more valuable than the alternative tested - which is a set of hand-crafted code mutation rules. But it's important to think about why an LLM is better. There are two big pitfalls for the hand-crafted rules. First, the hand-crafted rule has to make local changes - it's vanishingly improbable to be able to do something like "change < to <= in each of this series of if-statements" or "increase all constants by 1", whereas those are natural simple changes that an LLM might make. The second is that the random mutations have no concept of parsimonious code. There's nothing preventing it from generating code that does stuff like compute values and then neglect to return them, or multiply a previous computation by zero, or any number of other obviously useless variations. What the LLM brings to the table here is the ability to avoid pitfalls like the above. It writes code that is shaped sensibly - it can make a more natural set of edits than "just randomly delete an operator". But that's all it's doing. That's not, like the tweet quoted above called it, "generating genuinely new knowledge". Put it this way - assuming for a moment that Codey ending up with a higher score than StarCoder is not random chance, do you think it's because Codey has some greater understanding of the admissible-set problem, or because Codey generates a different set of minor code edits?