they are cleverly arranged / configured next-most-likely-token predictors, possibly with some clever procedures / attachments on top.

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danielmarkbruce 4 days ago | parent [-]

Nope. This isn't right.

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dijksterhuis 4 days ago | parent [-]

> "it's not just a next-token predictor because a bunch of the training isn't about predicting the next token."

clever procedures on top of the base transformer architecture.

i used simplified words/phrases to summarise the same thing you two were saying (the intent being: here's a version that may be digestible when discussing with others).

apparently that means i'm wrong though, no idea why because it seems you've decided to be dismissive rather than constructively elaborate on why this simplified and digestible version might be wrong :shrug:

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danielmarkbruce 4 days ago | parent | next [-]

They aren't predicting the next token. It's quite literally not a prediction.

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4 days ago | parent | next [-]

[deleted]

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gwerbin 4 days ago | parent | prev [-]

They're estimating a probability distribution over the next token, from which a sample is taken. Close enough.

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danielmarkbruce 4 days ago | parent [-]

It's not an estimation of something. It's a policy.

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gwerbin 3 days ago | parent [-]

Sure, you're right.

But it's a policy learned from a next-token prediction task. You could also call it an inferrer or generator or whatever. The point is that it takes as input a sequence of preceding tokens and emits one more token to continue the sequence.

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danielmarkbruce 3 days ago | parent [-]

The policy is not learned token by token during RLHF and RLVR. The reward model doesn't score token by token.

	▲	gwerbin 2 days ago \| parent [-]
		So? tokens are emitted one at a time according to the output distribution & sampler algorithm, and the next token distribution is a function of the preceding token sequence only. The process by which the output distribution is shaped doesn't change the core mental model, and doesn't reduce its value. It's a prediction in the jargonic sense that an inference about future values of a time series is broadly called "prediction", and it's relevant for reasoning about LLMs because they are fundamentally limited to converting tokens sequences into next-token predictive distributions, and that bears on how they can do what they do and what their limitations are. Nothing about the training process changes that.

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grebc 4 days ago | parent | prev [-]

We're talking to a RLVR bot.

Bad bot.