Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Embarrassingly simple self-distillation improves code generation

arxiv.org

Post reply on HN
Print view View original

141–150 of 227 posts

Re: Embarrassingly simple self-distillation improves code generation

#141

Post by ACCount37 »

oliver236 wrote: post #104
ACCount37 wrote: post #68

Earlier quoted context omitted.

I don't see much similarity? Unless you're looking at self-distillation in general and not just this use of it.

How not? I think the analogy is actually pretty specific to this paper, not just self-distillation in general. During sleep your brain replays experiences but noisy and distorted. The replays are often incoherent as narratives (dreams are weird). But the consolidation still works because the value isn't in the narrative coherence, it's in the activation patterns at each moment. Important pathways get strengthened, we…

This is a property of self-distillation.

Self-distillation shifts the behavior of the model towards that of the model + steering. As such, you don't strictly "need" the tokens to be in-domain for it to work. The logits are a vessel for transferring the steering into the model's internals.

The tokens can be gibberish. What transfers isn't whether they're gibberish or not, but how the flavor of model predictions, if given gibberish, differs from that of an unsteered version of itself.

In this specific case, the behavioral difference comes from the "temperature-shifted, truncated samples" in the "teacher" sampling strategy, and it is that difference that is internalized by the "student" model.

Re: Embarrassingly simple self-distillation improves code generation

#142

Post by osigurdson »

amelius wrote: post #53
user_7832 wrote: post #40

Earlier quoted context omitted.

> I love that we're still learning the emergent properties of LLMs! TBH, this is (very much my opinion btw) the least surprising thing. LLMs (and especially their emergent properties) are still black boxes. Humans have been studying the human brain for millenia, and we are barely better at predicting how humans work (or for eg to what extent free will is a thing). Hell, emergent properties of traffic was not understo…

Studies of LLMs belong in their own field of science, just like psychology is not being studied in the physics department.

That is a very interesting thought!

Re: Embarrassingly simple self-distillation improves code generation

#143

Post by devmor »

sgbeal wrote: post #98
jameshart wrote: post #93

Earlier quoted context omitted.

Give coding agents access to intellisense and syntax highlighting. Making coding agents spit out syntactically correct code token by token is like asking a human to code on a whiteboard.

> Give coding agents access to intellisense and syntax highlighting. i once asked an LLM if it could ingest code from an interactive session more easily if it were in appropriately-typed markdown fences and it said absolutely yes, and that the syntax highlighting fed to it that way helps it immensely. i was downright shocked that syntax highlighting was anything more than noise for them.

Why would this be surprising? That’s exactly how much of the code they were trained on is presented in PRs, Forums, etc.

Re: Embarrassingly simple self-distillation improves code generation

#144

Post by OxfordOutlander »

So... it's like a golfer who hits thousands of balls into an open field without ever once aiming for a hole. The relentless repetition flawlessly locks in their foundational muscle memory and basic swing mechanics, so when they finally step up to a real course, they don't have to waste a single thought on how to hold the club. Their basic swing is completely automatic - they can confidently take the creative, high-risk shot required to actually sink a hole-in-one.

Re: Embarrassingly simple self-distillation improves code generation

#145

Post by quotemstr »

auspiv wrote: post #102
khalic wrote: post #42

Earlier quoted context omitted.

Another example of the mindf@#$ these systems are: I was doing some fine tuning to a small model, take data fields and make a sentence out of it. I was running into mode collapse (basically when the AI simplifies too much and always output the same thing). I got unstuck by randomizing the field order for each row?!? At training, and now I'm thinking I should do the same at inference time...

apparently you can straight up duplicate/add/rearrange layers without changing any of the weights and get better results as well - https://dnhkng.github.io/posts/rys/

Neat!

> This is probably due to the way larger numbers are tokenised, as big numbers can be split up into arbitrary forms. Take the integer 123456789. A BPE tokenizer (e.g., GPT-style) might split it like: ‘123’ ‘456’ ‘789’ or: ‘12’ ‘345’ ‘67’ ‘89’

One of the craziest LLM hacks that doesn't get love is https://polymathic-ai.org/blog/xval/

xVal basically says "tokenizing numbers is hard: what if instead of outputting tokens that combine to represent numbers, we just output the numbers themselves, right there in the output embedding?"

It works! Imagine you're discussing math with someone. Instead of saying "x is twenty five, which is large" in words, you'd say "x is", then switch to making a whistling noise in which the pitch of your whistle, in its position within your output frequency range, communicated the concept of 25.00 +/- epsilon. Then you'd resume speech and say "which is large".

I think the sentiment is that today's models are big and well-trained enough that receiving and delivering quantities as tokens representing numbers doesn't hurt capabilities much, but I'm still fascinated by xVal's much more elegant approach.

Re: Embarrassingly simple self-distillation improves code generation

#146

Post by [deleted] »

AlphaAndOmega0 wrote: post #77
user_7832 wrote: post #40

Earlier quoted context omitted.

> I love that we're still learning the emergent properties of LLMs! TBH, this is (very much my opinion btw) the least surprising thing. LLMs (and especially their emergent properties) are still black boxes. Humans have been studying the human brain for millenia, and we are barely better at predicting how humans work (or for eg to what extent free will is a thing). Hell, emergent properties of traffic was not understo…

I'm a psychiatry resident who finds LLM research fascinating because of how strongly it reminds me of our efforts to understand the human brain/mind. I dare say that in some ways, we understand LLMs better than humans, or at least the interpretability tools are now superior. Awkward place to be, but an interesting one.

[deleted]

Re: Embarrassingly simple self-distillation improves code generation

#147

Post by guelo »

amelius wrote: post #53
user_7832 wrote: post #40

Earlier quoted context omitted.

> I love that we're still learning the emergent properties of LLMs! TBH, this is (very much my opinion btw) the least surprising thing. LLMs (and especially their emergent properties) are still black boxes. Humans have been studying the human brain for millenia, and we are barely better at predicting how humans work (or for eg to what extent free will is a thing). Hell, emergent properties of traffic was not understo…

Studies of LLMs belong in their own field of science, just like psychology is not being studied in the physics department.

¸That field is called Machine Learning.

Re: Embarrassingly simple self-distillation improves code generation

#148

Post by idiotsecant »

batshit_beaver wrote: post #121
danielmarkbruce wrote: post #113

Earlier quoted context omitted.

"Designed" is a bit strong. We "literally" couldn't design programs to do the interesting things LLMs can do. So we gave a giant for loop a bunch of data and a bunch of parameterized math functions and just kept updating the parameters until we got something we liked.... even on the architecture (ie, what math functions) people are just trying stuff and seeing if it works.

> We "literally" couldn't design programs to do the interesting things LLMs can do. That's a bit of an overstatement. The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enoug…

And all you have to do is write an infinite amount of code to cover all possible permutations of reality! No big deal, really.

Re: Embarrassingly simple self-distillation improves code generation

#149

Post by growpdifjkl »

batshit_beaver wrote: post #121
danielmarkbruce wrote: post #113

Earlier quoted context omitted.

"Designed" is a bit strong. We "literally" couldn't design programs to do the interesting things LLMs can do. So we gave a giant for loop a bunch of data and a bunch of parameterized math functions and just kept updating the parameters until we got something we liked.... even on the architecture (ie, what math functions) people are just trying stuff and seeing if it works.

> We "literally" couldn't design programs to do the interesting things LLMs can do. That's a bit of an overstatement. The entire field of ML is aimed at problems where deterministic code would work just fine, but the amount of cases it would need to cover is too large to be practical (note, this has nothing to do with the impossibility of its design) AND there's a sufficient corpus of data that allows plausible enoug…

[flagged]

Re: Embarrassingly simple self-distillation improves code generation

#150

Post by khalic »

quotemstr wrote: post #145
auspiv wrote: post #102

Earlier quoted context omitted.

apparently you can straight up duplicate/add/rearrange layers without changing any of the weights and get better results as well - https://dnhkng.github.io/posts/rys/

Neat! > This is probably due to the way larger numbers are tokenised, as big numbers can be split up into arbitrary forms. Take the integer 123456789. A BPE tokenizer (e.g., GPT-style) might split it like: ‘123’ ‘456’ ‘789’ or: ‘12’ ‘345’ ‘67’ ‘89’ One of the craziest LLM hacks that doesn't get love is https://polymathic-ai.org/blog/xval/ xVal basically says "tokenizing numbers is hard: what if instead of outputting…

I was having some issues with IP addresses representation, this might solve it
Post reply on HN