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Compressing Observation History into Agent Memory: Distilling Transformers into Recurrent Transformers

A research paper proposing a distillation approach to transfer the compression strategy of full-history transformers to recurrent variants.

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Compressing Observation History into Agent Memory: Distilling Transformers into Recurrent Transformers

By Philippe Weinzaepfel, Christian Wolf, Bülent Mert Sariyildiz, Guillaume Bono, Gianluca MonaciarXiv
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The authors propose a method to compress observation history into agent memory using a distillation approach. This allows training recurrent latent robotic memories with linear-time complexity while narrowing the performance gap to full-history transformers.

The method is designed for long-horizon streaming vision and robotics applications, such as map-free pose estimation.

Abstract

The authors propose a method to compress observation history into agent memory using a distillation approach. This allows training recurrent latent robotic memories with linear-time complexity while narrowing the performance gap to full-history transformers. The method is designed for long-horizon streaming vision and robotics applications, such as map-free pose estimation.

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transformersrecurrent-transformersdistillation-approachagent-memory-compressionrobotics-applicationsAI AgentsAgent MemoryLarge Language ModelsContent Engineering
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