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Grok 3: Another win for the bitter lesson

(www.thealgorithmicbridge.com)
129 points kiyanwang | 2 comments | 20 Feb 25 07:15 UTC | HN request time: 0.441s | source
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bccdee ◴[20 Feb 25 15:47 UTC] No.43116111[source]▶
The creation of a model which is "co-state-of-the-art" (assuming it wasn't trained on the benchmarks directly) is not a win for scaling laws. I could just as easily claim out that xAI's failure to significantly outperform existing models despite "throwing more compute at Grok 3 than even OpenAI could" is further evidence that hyper-scaling is a dead end which will only yield incremental improvements.

Obviously more computing power makes the computer better. That is a completely banal observation. The rest of this 2000-word article is groping around for a way to take an insight based on the difference between '70s symbolic AI and the neural networks of the 2010s and apply it to the difference between GPT-4 and Grok 3 off the back of a single set of benchmarks. It's a bad article.

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1. starspangled ◴[21 Feb 25 01:30 UTC] No.43123016[source]▶
> The creation of a model which is "co-state-of-the-art" (assuming it wasn't trained on the benchmarks directly) is not a win for scaling laws.

Just based on the comparisons linked in the article, it's not "co-state-of-the-art", it's the clear leader. You might argue those numbers are wrong or not representative, but you can't accept them then claim it's not outperforming existing models.

replies(1): >>43134551 #
2. bccdee ◴[21 Feb 25 23:47 UTC] No.43134551[source]▶
The leader, perhaps, but not by a large margin, and only on these sample benchmarks. "Co-state-of-the-art" is the term used in the article, and I'm going to take that at face value.