Interesting... Naively I'd assume you'd have a pretty unfair advantage on quality if you have materially more information dense tokens.
replyThat doesn't really appear to be the case as GPT and Anthropic models appear evenly matched despite Anthropic encoding the same text into almost ~2x the tokens...
I'd also - naively - assume this would make training their models more expensive. Though inference now dominates, and they'd probably rather have more tokens than less (to charge you for them at future 80% margins).
If a given paragraph gets encoded into twice as many tokens, that means the model gets twice as many matmuls to process it. The amount of compute thrown at the problem is increased (everything else constant), which may improve the quality of the result. This is believed to be one of the reasons that 'thinking' tokens improve quality. For long tasks it will lead to more context compactions though which will harm the quality to some degree as well.
replyIt would be nice if inference could somehow perform token generation using "contractions" of "fluffy" tokens, where combining those tokens doesn't decrease nuance but provides additional efficiency. That may already be happening - I haven't looked at the most modern methods of inference in a long long time.
replymore dense tokens means more stuff to fit into the embedding space which is per token, so more work to disentangle later
replyyou use the wrong word
replythe Anthropic tokenizer is not worse, its more expensive/verbose
So, worse? Because we benchmark off token use when talking about token use, and everyone else understood that.
replyThe most important feature of a tokenizer is dividing the inputs into independent values that the neural network can work with. It's not the size.
replyI mean it might lead to better performance on the model side. So the tokenizer is better but more expensive.
replyIt’s better for them
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