9 ms·

Nope. This isn't right.

dijksterhuis 12d ago
> "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:
- danielmarkbruce 12d ago
  They aren't predicting the next token. It's quite literally not a prediction.
  - deleted 12d ago
    [deleted]
  - gwerbin 12d ago
    They're estimating a probability distribution over the next token, from which a sample is taken. Close enough.
    - danielmarkbruce 12d ago
      It's not an estimation of something. It's a policy.
      - gwerbin 11d ago
        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.
        danielmarkbruce 11d ago
        The policy is not learned token by token during RLHF and RLVR. The reward model doesn't score token by token.
        gwerbin 10d ago
        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.
- grebc 12d ago
  We're talking to a RLVR bot. Bad bot.

> "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:

danielmarkbruce 12d ago
They aren't predicting the next token. It's quite literally not a prediction.
- deleted 12d ago
  [deleted]
- gwerbin 12d ago
  They're estimating a probability distribution over the next token, from which a sample is taken. Close enough.
  - danielmarkbruce 12d ago
    It's not an estimation of something. It's a policy.
    - gwerbin 11d ago
      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.
      - danielmarkbruce 11d ago
        The policy is not learned token by token during RLHF and RLVR. The reward model doesn't score token by token.
        gwerbin 10d ago
        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.
grebc 12d ago
We're talking to a RLVR bot. Bad bot.