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[2602.13466] Language Model Memory and Memory Models for Language
Summary
The paper explores the limitations of memory in language models, proposing a new architecture that enhances memory formation through combined training objectives.
Why It Matters
Understanding how language models retain information is crucial for improving their efficiency and effectiveness. This research highlights the need for better memory architectures, which can lead to advancements in natural language processing and machine learning applications.
Key Takeaways
- Language model embeddings often contain minimal input information.
- Autoencoders can achieve nearly perfect memory formation compared to traditional models.
- A new encoder-decoder architecture can improve computational efficiency.
- Combining causal training with information retention objectives enhances memory capabilities.
- Next token prediction alone is insufficient for effective memory formation.
Computer Science > Computation and Language arXiv:2602.13466 (cs) [Submitted on 13 Feb 2026] Title:Language Model Memory and Memory Models for Language Authors:Benjamin L. Badger View a PDF of the paper titled Language Model Memory and Memory Models for Language, by Benjamin L. Badger View PDF HTML (experimental) Abstract:The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is widely employed but not well characterized. We find that language model embeddings typically contain relatively little input information regardless of data and compute scale during training. In contrast, embeddings from autoencoders trained for input regeneration are capable of nearly perfect memory formation. The substitution of memory embeddings for token sequences leads to substantial computational efficiencies, motivating the introduction of a parallelizable encoder-decoder memory model architecture. Upon causal training these models contain information-poor embeddings incapable of arbitrary information access, but by combining causal and information retention objective functions they learn to form and decode information-rich memories. Training can be further streamlined by freezing a high fidelity encoder followed by a curriculum training approach where decoders first learn to process memories and then learn to additionally predict next tokens. We introduce the perspective that next token prediction training al...
