Learning What to Remember: Adaptive Probabilistic Memory Retention for Memory-Efficient Language Models (2025.findings-emnlp)
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| Challenge: | Adaptive Retention is a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict global budget M. |
| Approach: | They propose a probabilistic token selection mechanism that learns which representations to keep under a strict global budget M. |
| Outcome: | The proposed method reduces memory usage by 35–45% while improving throughput by 1.8. |
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