# Accelerating Neural Self-Improvement via Bootstrapping

Source: [Accelerating Neural Self-Improvement via Bootstrapping](<https://www.sota2.com/research/paper/68fa6f2d1e209131cea44436>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:15:37.285Z

Authors: Kazuki Irie, J\\"urgen Schmidhuber

Published: 2023-05-02T15:52:34.000Z

## Abstract

Few-shot learning with sequence-processing neural networks (NNs) has recently attracted a new wave of attention in the context of large language models. In the standard N-way K-shot learning setting, an NN is explicitly optimised to learn to classify unlabelled inputs by observing a sequence of NK labelled examples. This pressures the NN to learn a learning algorithm that achieves optimal performance, given the limited number of training examples. Here we study an auxiliary loss that encourages further acceleration of few-shot learning, by applying recently proposed bootstrapped meta-learning to NN few-shot learners: we optimise the K-shot learner to match its own performance achievable by observing more than NK examples, using only NK examples. Promising results are obtained on the standard Mini-ImageNet dataset. Our code is public.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.01547>)

[PDF](<https://arxiv.org/pdf/2305.01547.pdf>)