The Serial Scaling Hypothesis
cs.LG, cs.CC, stat.ML
Submitted: 2025-07-16
Updated: 2026-10-12
Terminology
Sources
- GPT-4 Technical Report
- Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models
- Theoretical limitations of multi-layer Transformer
- The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity
- Transformers in Uniform TC$^0$
- On the Measure of Intelligence
- Training Verifiers to Solve Math Word Problems
- Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- Efficiently Modeling Long Sequences with Structured State Spaces
- Denoising Diffusion Probabilistic Models
- Training Compute-Optimal Large Language Models
- How Far is Video Generation from World Model: A Physical Law Perspective
- Scaling Laws for Neural Language Models
- BIG-Bench Extra Hard
- 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
- O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions
- Chain of Thought Empowers Transformers to Solve Inherently Serial Problems
- Why Deep Neural Networks for Function Approximation?
- SDXL-Lightning: Progressive Adversarial Diffusion Distillation
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks