Channel Selection for Mixture-of-Experts Decoding Needs No Calibration Data
The paper argues that channel selection for Mixture-of-Experts decoding needs no calibration data. A calibrated threshold does not fix read budget, since Prox calibrated for 18% reads only 12.9% on average. Top-k selection matches or beats threshold versions in 40 of 43 comparisons at 13% and 18% budgets and avoids repetition-loop collapse on Gemma 4. A weight-derived reader retains 98.1% of full-model accuracy at 17.8% of routed-expert reads, with Triton kernels 1.60 to 2.69x faster than optimized vLLM.
Open original source huggingface.coOne primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper argues that channel selection for Mixture-of-Experts decoding needs no calibration data. A calibrated threshold does not fix read budget, since Prox calibrated for 18% reads only 12.9% on average. Top-k selection matches or beats threshold versions in 40 of 43 comparisons at 13% and 18% budgets and avoids repetition-loop collapse on Gemma 4. A weight-derived reader retains 98.1% of full-model accuracy at 17.8% of routed-expert reads, with Triton kernels 1.60 to 2.69x faster than optimized vLLM.
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Source claims not independently checked; results are reported across three post-trained MoE models and specific benchmarks.
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Key findings from the stored brief
- A calibrated threshold does not fix read budget; Prox calibrated for 18% read only 12.9% on average.
- Top-k selection matches or beats threshold versions in 40 of 43 comparisons at 13% and 18% budgets.
- Top-k versions avoid the repetition-loop collapse that calibrated thresholds cause on Gemma 4.
- The weight-derived reader keeps 98.1% of full-model accuracy at 17.8% of routed-expert reads.
Source claims not independently checked; results are reported across three post-trained MoE models and specific benchmarks.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
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Why this may matter
Calibration-free channel selection could simplify deploying MoE models for interactive decoding, where loading expert weights is the bottleneck. It suggests calibrated thresholds may not deliver the budget or accuracy gains they are used for.
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Source claims not independently checked; results are reported across three post-trained MoE models and specific benchmarks.
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