Continual Learning: Three Approaches That Work

Sun, 20 Sep 2026 00:00:00 +0800

TL;DR

Keeping a model learning after training ends usually means writing to an external memory file, not updating the model’s own weights - the way a fruit fly’s brain may encode navigation directly into synaptic strength rather than a sustained loop of neural firing. Closing that gap is the harder, weight-level version of recursive self-improvement (RSI).
More training does not reliably mean a better model: Continuously training a model during inference risks catastrophic forgetting (the model overwrites what it already knew).
Three real approaches exist for updating weights continuously: reinforcement learning (narrow, moves only a small targeted slice), LoRAs (discardable bolt-on weight blocks), and differentiable plasticity (a second system decides which of the first system’s weights are editable - the most elegant option, but the slowest to train).

Key Takeaways

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