Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

Alexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, after helping research the Yann LeCun and Randall Balestriero episode on interpolation.

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Alexander treats inference as the thread running through modern machine learning: once you have a model, what does it cost to get an answer out of it? He works through Monte Carlo, GFlowNets, energy-based models, diffusion, normalising flows and flow matching, with four short explainers he recorded himself. He is blunt about energy-based models: you can sample from them in principle, but it is rarely worth the compute. JEPA and "world model", he says, are closer to branding than to technical categories.

Next: theories of deep learning, none of which he thinks predicts enough yet to guide practice, then reinforcement learning.

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0:00 Cold open: information is expensive

0:51 Welcome back, Alexander Mattic

2:08 Alexander's research background

2:50 Inference: densities, sampling and Monte Carlo

6:42 GFlowNets, energy functions and MCMC

9:45 Explainer: energy-based models

11:03 Why model a density at all?

17:30 From learned energies to flow matching

25:08 Explainers: diffusion and normalising flows

28:33 Are energy-based models generative?

33:22 JEPA, contrastive learning and collapse

41:13 Why non-language modalities need flows

44:51 Inference as search: branch and bound

49:43 Q-learning and delayed consequences

55:14 Flow matching, optimal transport, Fokker-Planck

1:00:03 Explainer: flow matching

1:01:49 AlphaFold, latents and scale versus architecture

1:07:52 Two families of deep learning theory

1:15:04 What a good theory would predict

1:23:53 The manifold hypothesis and compression

1:28:25 Is reward enough?

1:32:01 Control theory versus reinforcement learning

1:37:22 The Bitter Lesson and expensive information

1:42:08 Constrained RL: the constrained MDP toolbox

1:50:12 Creativity as constrained search

1:55:44 Reality is protean: when abstractions hold

2:00:32 What is a world model?

2:04:38 Prediction is not control

2:08:13 Robot demos, MPC and reliability

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REFERENCES:

[6:55] GFlowNets (Bengio et al., 2021)

https://arxiv.org/abs/2106.04399

[38:46] Contrastive Self-Supervised Learning (Anand, 2020)

https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html

[38:56] LeJEPA (Balestriero and LeCun, 2025)

https://arxiv.org/abs/2511.08544v3

[47:10] RL for Node Selection in Branch-and-Bound (Mattick)

https://openreview.net/forum?id=0ez68a5UqI

[56:20] Flow Matching for Generative Modeling

https://arxiv.org/abs/2210.02747v2

[1:12:41] Disentangling feature and lazy training in deep neural networks

https://arxiv.org/abs/1906.08034v4

[1:31:05] Reward is enough (Silver)

https://doi.org/10.1016/j.artint.2021.103535

[1:35:12] Learning ReLU networks to high uniform accuracy is intractable (Berner et al.)

https://arxiv.org/abs/2205.13531v2

[1:40:20] Dota 2 with Large Scale Deep RL

https://arxiv.org/abs/1912.06680v1

[1:45:41] Constrained Update Projection for Safe Policy Optimization (Yang et al., 2022)

https://arxiv.org/abs/2209.07089

[1:46:11] SafeMPO (ICLR 2026)

https://openreview.net/forum?id=1m0EU6QXj6

[1:50:17] Why Creativity Cannot Be Interpolated

https://archive.mlst.ai/paper/why-creativity-cannot-be-interpolated/

[1:51:39] Invalid Action Masking (Huang and Ontañón)

https://arxiv.org/abs/2006.14171

[2:00:04] Probability Theory: The Logic of Science (Jaynes, 2003)

https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99

[2:01:53] Training Agents Inside of Scalable World Models (Hafner et al., 2025)

https://arxiv.org/abs/2509.24527v1

[2:03:43] World Models (Ha and Schmidhuber, 2018)

https://arxiv.org/abs/1803.10122v4