Falcon Framework: Adaptive Memory Updates for Efficient Continual Learning
burny_tech · x · 2026-09-02
This paper investigates optimizing Fast-weight attention mechanisms for continual learning tasks.
Key Insights & Contributions:
- Fast-weight models aim to reduce attention costs by continuously rewriting a fixed-size memory.
- The authors propose that this rewrite should behave more like online learning, adjusting based on the model's predictions versus actual outcomes.
- Introduces adaptive memory updates that determine the rate of learning, forgetting, and rehearsal while maintaining constant memory size and training efficiency.
- The proposed framework (Falcon family) separates temporal alignment, plasticity, forgetting, and bounded rehearsal, showing competitive performance in language modeling and improved length extrapolation on variable-digit addition.
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