Reliable LLM Computation with ~400K Parameters: Discrete Execution Boundary
kostrubaty · reddit · 2026-08-12
The author explores how to build a tightly coupled dual runtime—comprising a neural agent and a deterministic harness—via the Discrete Execution Boundary to solve reliability issues in LLM computation and state tracking.
By training a tiny model within a WebGPU environment, the author drew key conclusions:
- Semantic Token Advantage: Dense canonical IR beats plain English + ByteLevel BPE by 40 points on compositional holdouts at matched token budgets.
- Depth Extrapolation Limits: Depth does not extrapolate in a single forward pass, but moving depth into the number of local calls under an external scheduler achieves 100% accuracy up to depth 16.
- Identifier Handling: Learned embeddings shouldn't be used as identifiers; identity must be a separate, exact channel.
- Tiny Parameter Scale: The learned part of the stack (intent compiler) requires only 126K parameters, with a whole-system floor of just 400K parameters set by quantity pointer stages.
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