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Wrong and short-sighted take given that the LLM explores serially learning along the way, and can tool use and change code arbitrarily. It seems to currently de
by karpathy 6mo ago
Wrong and short-sighted take given that the LLM explores serially learning along the way, and can tool use and change code arbitrarily. It seems to currently default to something resembling hyperparameter tuning in absence of more specific instructions. I briefly considered calling the project “autotune” at first but I think “autoresearch” will prove to be the significantly more appropriate name.
- corndoge 6mo agoWould you say it's fair to describe autoresearch as a form of neural architecture search? I am curious what you think the core differences are between them.
- kraddypatties 6mo agoI can believe that in the long run. Does the agent have access to arxiv (a brief skim of the README didn't have an answer)? If not, it could be that the current approach of relying on the model's weights only is resulting in the perceived local optimum of hyperparameter tuning. Anecdotally, we built a little MCP for arxiv to help with our internal research, noticed a significant boost in the diversity of methods (architecture or otherwise) Claude and friends were able to reference.
- touristtam 6mo agocare to share?
- westurner 6mo agoIs there a cost to converge? And how much does it vary with the random seed? Re: OpenCogPrime:EconomicAttentionAllocation https://news.ycombinator.com/item?id=45518074 https://news.ycombinator.com/item?id=45518074 and something about eWASM (edit) https://news.ycombinator.com/item?id=47171887 https://news.ycombinator.com/item?id=47171887 .. from https://news.ycombinator.com/item?id=46825026 https://news.ycombinator.com/item?id=46825026 re: eWASM and costed opcodes for agent efficiency
- achierius 6mo agoOut of curiosity, what sort of things have you seen it do that better fit 'autoresearch' than 'autotune' thus far? Optimizations it made that wouldn't be been surfaced by an autotune system, I suppose.
- karpathy 6mo agoThe most recent round of autoresearch (round 2) which decreased "time to GPT-2" from 1.8 hours to 1.65 hours had some examples. I adjusted the program.md to "look at modded nanogpt project and draw inspirations from there for things to try" and it came back with a bunch of tuning, but also tried and implemented new architecture changes, some of which actually helped including the smear gate and the backout skip connection. These are not just hyperparameters, they are new PyTorch code. I'm now working on a more general system that can have a queue of ideas that could be sourced from archive papers, github repos, etc.
- rfw300 6mo agoDo you have a sense of whether these validation loss improvements are leading to generalized performance uplifts? From afar I can't tell whether these are broadly useful new ideas or just industrialized overfitting on a particular (model, dataset, hardware) tuple.
- whiplash451 6mo agoWhy set the bar higher on generalization for autoresearch vs the research humans generally do?
- youngprogrammer 6mo agoindustrialized overfitting is basically what ML researchers do
- johndough 6mo agoDid you consider providing the LLM with a framework for automatic hyperparamter tuning? This would free up its capacity to focus on the more important architectural decisions.
- saberience 6mo agoHave you actually used LLMs for non trivial tasks? They are still incredibly bad when it comes to actually hard engineering work and they still lie all the time, it's just gotten harder to notice, especially if you're just letting it run all night and generate reams of crap. Most people are optimizing for terrible benchmarks and then don't really understand what the model did anyone and just assume it did something good. It's the blind leading the blind basically, and a lot of people with an AI-psychosis or delusion.
- nfg 6mo agoDo you realise who you’re replying to?
- _menelaus 6mo agololololol
- emp17344 6mo agoWhy should we care that he’s famous?
- nfg 6mo agoFame doesn’t enter it - the point is Karpathy has about as strong a claim as anyone to having “actually used LLMs for non trivial tasks”.
- deleted 6mo ago[deleted]
- nurettin 6mo agoThat is not the case at all, considering that he himself started using and tweeting about llms for coding fairly recently. He's probably less experienced in that area than most people who started using claude cli last year. He is a researcher who understands neural networks and their architectures exceptionally well. That is all.
- deleted 6mo ago[deleted]
- Drupon 6mo agotfw le AI guy has LLM psychosis. We're cooked
- janalsncm 6mo agoI think we need to separate theory from practice. In theory, it can edit the training loop and come up with novel techniques. That is interesting. In practice, the vast majority of the changes that auto research actually made would have been found much faster with BO if properly parameterized. You do not need an LLM to find a better batch size or learning rate.
- prpl 6mo agoI’d always hoped something like this could take advantage of FPGAs directly
- vasco 6mo agoFPGAs won't rebuild fast enough for it to matter vs software simulation I'd wager. Even FPGA-in-CPU has been a dream for decades and there you have more time for some workloads, still never was commercially viable for general computing.
- jmalicki 6mo agoThere was research a few years back that tried doing something like this with an FPGA, and they found that their algorithm actually exploited defects in the particular chip (not the model, the actual single specific chip) they were using to use electrical interference for computation that shouldn't have worked on paper. They could not reproduce their design on another FPGA of the same model from the same lot.
- kotama7 6mo ago[flagged]
- throwaway132448 6mo agoNaming things is your primary contribution to AI so well done for deliberating on it. I disagree with the outcome though. Autotune would have been much more fitting.
- DoctorOetker 6mo agoI wonder about the following: To calculate an gradient step, in practice one doesn't accumulate the gradient for the full corpus, but updates the weights on mini-batches. Suppose one runs conventional gradient descent on minibatches multiple times with different starting seeds, and then considers a set of pre-trained models M_i From a random starting point we thus have an idea of the desired end-region in weightspace (lets say a gaussian cloud fit to the final M_i's). Then it seems like one could score update strategies by how much a single iteration has approached the gaussian cloud, by scoring just the approach on a number of minibatches or just a few update iterations. Instead of searching update strategy space by waiting until pretraining has finished for each candidate update strategy. Only the candidate strategies that perform well enough on 1 or a few iterations would be considered worthy of further consideration, those that pass (a lower number of candidates) are then inspected for approach to the gaussian target after another round of iterations etc. It seems like it should be possible to optimize the optimization iteration loop, by running it just once for many candidates and observing their convergence to the known desired end region.
- DoctorOetker 6mo agoThe dataset climbmix 400b looks like it is 600GB, it would be neat if someone could host this in compressed form, given that LLM can be used to compress, even having a small LLM compress it would perform better than classical compression algorithms, why is this approach not used within the ML community? Or is it the "anyone who means anything in the field, has access to high bandwidth anyway"?