Titans: Learning to Memorize at Test Time
The paper presents the Titans architecture, which integrates short-term attention and long-term memory, enabling larger context windows and outperforming existing models like Transformers in various tasks while supporting faster training.
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The paper titled "Titans: Learning to Memorize at Test Time" introduces a new neural long-term memory module designed to enhance the performance of attention mechanisms in machine learning models. Traditional recurrent models compress data into a fixed-size memory, while attention mechanisms capture dependencies across the entire context window but are limited by their quadratic cost, restricting context length. The proposed Titans architecture combines short-term attention with long-term memory, allowing for effective utilization of historical context during inference. This approach enables faster parallel training and inference while scaling to context windows larger than 2 million tokens. Experimental results demonstrate that Titans outperform both Transformers and modern linear recurrent models across various tasks, including language modeling, common-sense reasoning, genomics, and time series analysis. The study emphasizes the importance of integrating memory into neural architectures to improve accuracy in complex tasks.
- The Titans architecture combines short-term attention and long-term memory for improved performance.
- It allows for larger context windows, exceeding 2 million tokens.
- Experimental results show Titans outperform existing models like Transformers in multiple tasks.
- The architecture supports fast parallel training and inference.
- The study highlights the significance of memory integration in machine learning models.
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9 comments
and i tried to unpack it a bit here https://wdmn.fr/rank-1-take-on-rwkv7s-in-context-learning/
1. The key data point seems to be Figure 6a. Where it compares performance on BABILong and claims Titans performance is at ~62%, as compared to GPT-4o-mini at ~42% for 100k sequence length.
However, GPT-4o and Claude are missing in this comparison - maybe because they perform better ?
2. There is no example provided of the Neural Memory Module in action. This is the first question I would ask of this paper.
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