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Interactive explainers of recent ML papers. Curated and community-generated.
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LoRA: Low-Rank Adaptation of Large Language Models
WikiSQL: 74.0%Edward J. Hu, Yelong Shen +6
An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train…
Denoising Diffusion Probabilistic Models (DDPM)
CIFAR10: 9.46Jonathan Ho, Ajay Jain, Pieter Abbeel
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium…
Spurious Rewards: Rethinking Training Signals in RLVR
MATH-500: 70.8%Rulin Shao, Shuyue Stella Li +12
We show that reinforcement learning with verifiable rewards (RLVR) can elicit strong mathematical reasoning in certain language models even with spurious rewards that have…
s1: Simple Test-Time Scaling
AIME24: 56.7%Niklas Muennighoff, Zitong Yang +8
Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability…
Generative Adversarial Nets
MNIST: 225 ± 2Ian J. Goodfellow, Jean Pouget-Abadie +6
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data…
Inside MuZero: Mastering the World Without Knowing the Rules
Atari 57 games (large data): 2041.1%Julian Schrittwieser, Ioannis Antonoglou +10
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge…
Training Compute-Optimal Large Language Models
MMLU: 67.6%Jordan Hoffmann, Sebastian Borgeaud +20
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are…
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Wikipedia: 15%Tri Dao, Daniel Y. Fu +3
Transformers are slow and memory-hungry on long sequences, since the time and memory complexity of self-attention are quadratic in sequence length. Approximate attention methods…
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Language Modeling: 5xAlbert Gu, Tri Dao
Foundation models, now powering most of the exciting applications in deep learning, are almost universally based on the Transformer architecture and its core attention module.…
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
GSM8K: 57.0%Jason Wei, Xuezhi Wang +7
We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex…
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Reddit TL;DR: 61.0%Rafael Rafailov, Archit Sharma +4
While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the…
Proximal Policy Optimization (PPO)
Average Normalized Score: 0.82John Schulman, Filip Wolski +3
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a…
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL
AIME 2024: 79.8%DeepSeek-AI, Daya Guo +198
General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and…
Attention Is All You Need — Explaining the Transformer
BLEU: 28.4Ashish Vaswani, Noam Shazeer +6
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also…