Is the Softmax Bottleneck Holding Back AI Progress?
Neural language models face a hidden bottleneck limiting both expressivity and optimization. Is it time for a rethink on LM design?
Read the full articleYou might also wanna read
Systematic Evaluation of Deep Learning Optimizers Reveals Limited Speedup Over AdamW in Language Model Pretraining
AdamW has long been the dominant optimizer in language model pretraining, despite numerous claims that alternative optimizers offer 1.4 to 2
arxiv.org·10mo ago
LK Losses: A New Training Objective to Optimize Acceptance Rate in Speculative Decoding for LLMs
Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate
arxiv.org·1mo ago
Why LLMs Are So Expensive: The Quadratic Cost of Dense Attention — and How Subquadratic Claims to Fix It
Dense attention's quadratic compute scaling has been the hidden cost driver behind enterprise AI since 2017. Subquadratic's SubQ model posts
shashi.co·16d ago
Understanding Continuous Batching in Large Language Models: From Attention Mechanisms to Throughput Optimization
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Flash-MSA Method Aims to Speed Up AI Training on Million-Token Sequences
Researchers have introduced Flash-MSA, a technique designed to accelerate the training of large language models on very long sequences of up
Research Directions for Overcoming Memory and Interconnect Challenges in Large Language Model Inference Hardware
Large Language Model (LLM) inference is hard. The autoregressive Decode phase of the underlying Transformer model makes LLM inference fundam
arxiv.org·5mo ago
Comments
Sign in to join the conversation.
No comments yet. Be the first.