zlacker

Yeah, Yann LeCun is just some luddite lol

>>heavys+(OP)
I don't think he's a luddite at all. He's brilliant in what he does, but he can also be wrong in his predictions (as are all humans from time to time). He did have 3 main predictions in ~23-24 that turned out to be wrong in hindsight. Debatable why they were wrong, but yeah.

In a stage interview (a bit after the "sparks of agi in gpt4" paper came out) he made 3 statemets:

a) llms can't do math. They can trick us with poems and subjective prose, but at objective math they fail.

b) they can't plan

c) by the nature of their autoregressive architecture, errors compound. so a wrong token will make their output irreversibly wrong, and spiral out of control.

I think we can safely say that all of these turned out to be wrong. It's very possible that he meant something more abstract, and technical at its core, but in the real life all of these things were overcome. So, not a luddite, but also not a seer.

>>Nitpic+Z3
Have this shortcomings of llms been addressed by better models or by better integration with other tools? Like, are they better at coding because the models are truly better or because the agentic loops are better designed?

>>gjadi+u4
100% by better models. Since his talk models have gained more context windows (up to usable 1M), and RL (reinforcement learning) has been amazing at both picking out good traces, and taught the LLMs how to backtrack and overcome earlier wrong tokens. On top of that, RLAIF (RL with AI feedback) made earlier models better and RLVR (RL with verifiable rewards) has made them very good at both math and coding.

The harnesses have helped in training the models themselves (i.e. every good trace was "baked in" the model) and have improved in enabling test time compute. But at the end of the day this is all put back into the models, and they become better.

The simplest proof of this is on benchmarks like terminalbench and swe-bench with simple agents. The current top models are much better than their previous versions, when put in a loop with just a "bash tool". There's a ~100LoC harness called mini-swe-agent [1] that does just that.

So current models + minimal loop >> previous gen models with human written harnesses + lots of glue.

> Gemini 3 Pro reaches 74% on SWE-bench verified with mini-swe-agent!

[1] - https://github.com/SWE-agent/mini-swe-agent

>>heavys+(OP)
You don't understand Yann's argument. It's similar to Richard Sutton's, in that these things aren't thinking, they're emulating thinking, and the weak implicit world models that get built in the weights are insufficient for true "AGI."

This is orthogonal to the issue of whether all ideas are essentially "remixes." For the record I agree that they are.

>>gjadi+u4
Fundamentally these shortcomings cannot be addressed.

They can and are improved (papered over) over time. For example by improving and tweaking the training data. Adding in new data sets is the usual fix. A prime example 'count the number of R's in Strawberry' caused quite a debacle at a time where LLM's were meant to be intelligent. Because they aren't they can trip up over simple problems like this. Continue to use an army of people to train them and these edge cases may become smaller over time. Fundamentally the LLM tech hasn't changed.

I am not saying that LLM's aren't amazing, they absolutely are. But WHAT they are is an understood thing so lets not confuse ourselves.

>>Curiou+4Q
I agree with Yann