alvelda proposes embedding agent memories into model weights, like sleep consolidates memories
alvelda · x · 2026-11-06
alvelda sketches a directional idea: today's practice trains a static base model, while the runtime harness accrues memories attached to expanded prompts, with all the context-management burden that entails. Instead, he suggests using memories to run additional training so they're embedded in the model itself — removing the need for memory-harness context bloat once the base model is extended — analogous to how human brains consolidate memories during sleep and dreaming.
Related event: Alvelda Proposes Training Agent Memory into Model Weights(2 posts)→
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