Efficient Scaling of Language Models and Adaptive Networks

Introduction

The field of language models and adaptive networks is rapidly evolving, with a focus on efficient scaling and dynamic adaptation. Recent developments have led to the emergence of Mixture of Experts (MoE) models, which achieve high performance while reducing training and inference costs.

General Direction

The field is moving towards the development of more efficient and adaptive models, with a focus on reducing computational resources and improving performance. This is being achieved through the use of MoE models, which activate only a subset of parameters for each input token, as well as through the development of reusable and stitchable networks that can dynamically adapt to changing resource constraints.

Noteworthy Papers

  • The dots.llm1 Technical Report presents a large-scale MoE model that achieves state-of-the-art performance while reducing training and inference costs.
  • The ReStNet paper proposes a reusable and stitchable network that dynamically constructs a hybrid network by stitching two pre-trained models together, allowing for flexible accuracy-efficiency trade-offs at runtime.

Sources

dots.llm1 Technical Report

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices

DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts

A Hierarchical Probabilistic Framework for Incremental Knowledge Tracing in Classroom Settings

Test-Time Adaptation for Generalizable Task Progress Estimation

Mastery Learning Improves Performance on Complex Tasks on PCP Literacy Test

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