Concept: small models with an external 'map' instead of billions of parameters
FluidModeNetwork · reddit · 2026-10-21
A Reddit concept post argues for building models that 'know how to navigate' rather than models that memorize everything, using an exam cheat-sheet analogy: with unlimited notes, what the student personally knows barely matters. The proposed pipeline: (1) an RLM builds a 'map' from a database or library; (2) a small tool-calling model probes the map; (3) the small model answers questions from what the map returns. Claimed pros: repeatable results since the map stays fixed while weights change; the map lives in storage, not VRAM/RAM; dramatically less training needed; potentially a cheap path to fluency in any language or skill. Cons: trading RAM for storage, and conceptually organizing the map may require new methods. Purely a concept, not implemented.
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