■ Block 01 / research

Research

Foundations other work depends on. Each entry explains what it is, why it is load-bearing, its open problems, and a reviewed timeline of progress.

Propose an entry

ArchitecturesAlgorithmActive

The transformer architecture

The attention-based neural network design behind modern language models, and the quadratic cost of attention that drives work on exact, approximate and recurrent alternatives.

2 problems15 dependents0 maintainers

Machine LearningLibraryMature

PyTorch

The dominant open-source deep learning framework: tensors, autograd and a compiler stack.

0 problems4 dependents0 maintainers

Training & OptimizationAlgorithm

Research

The transformer architecture

PyTorch

The transformer architecture

PyTorch

Reverse-mode automatic differentiation

RL benchmarks and evaluation: Atari 100k, ProcGen, rliable

Offline reinforcement learning

Model-based RL and world models (MuZero, Dreamer)

Monte Carlo tree search and AlphaZero

RL with verifiable rewards and GRPO for reasoning models

RLHF and preference optimization (DPO)

Policy gradient methods and PPO

Maximal update parametrization (μP) and hyperparameter transfer

Adam, AdamW and the optimizer zoo

Language-model evaluation methodology

Neural scaling laws and compute-optimal training

State-space sequence models (S4, Mamba, Mamba-2)

Mixture-of-experts routing

Normalization layers (BatchNorm, LayerNorm, RMSNorm)

Mechanistic circuits and causal interventions

Sparse autoencoders for interpretability