route-token-token-between-base-model-sft-expert TEC-0192 · proposedRoute token by token between base model and SFT expert
Train a lightweight two-stage router on the base model's hidden states, labelled by tokens where the base's greedy prediction is wrong and the expert's is right, then at decoding mix the two next-token distributions with momentum smoothing
Proposed. Filed by the drafting stage because a paper introduces or tests it. Nobody has vouched for it. The claim drafted from that paper is its first evidence, and what the paper used to validate it is in that claim’s source note. Say whether it held up for you, below.
Train a lightweight two-stage router on the base model's hidden states, labelled by tokens where the base's greedy prediction is wrong and the expert's is right, then at decoding mix the two next-token distributions with momentum smoothing Filed by the drafting stage from CPR for LLMs: Critical-Point Routing against Catastrophic Forgetting in Domain Adaptation (2608.30158) with status proposed: the paper introduces or tests it, and the claim drafted from the paper is its first evidence. The paper validated it by: Four model-domain settings, six benchmarks; compared against vanilla SFT, DFT, EAFT, LfU, RouteLLM, Switch Generation, ensemble and contrastive decoding sharing the same base and expert; highest overall average in all settings, plus ablations on router architecture, momentum and thresholds, and wall-clock latency (1.40-1.44x expert-only vs 1.89-2.25x ensemble).. Nobody has vouched for it; add support or contest it on this page.
Does it work?
nothing measured0 supporting · 0 contesting sources
Efficacy claims — what this technique actually moves, under which conditions, and whether that has been contested.
No efficacy claim filed yet. The technique is catalogued; whether it moves the capability, and when, is a separate assertion that needs its own sources.
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Sources
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