Sigmoid gating treats each expert as an independent on/off gate, better when multiple experts should activate without competing for a single softmax probability mass.
Imagine splitting one pizza where bigger slices for one person mean smaller slices for everyone else, the group shares one fixed total. Now imagine each person decides independently whether to eat; several can eat fully without stealing from others. MoE routing gates work the same way: one style forces experts to compete for one budget; the other lets each expert switch on by itself.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Concept explanation~2 min read
Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.
Gating is not a cosmetic choice, it defines whether experts cooperate as independent specialists or compete for a fixed probability budget. Softmax and sigmoid are the two dominant routing squashing functions in MoE, and conflating them is a frequent interview miss.
This question asks when sigmoid is the better fit. The correct answers all share one theme: independent activation without global competition. The wrong answers describe softmax behavior, sometimes inverted, as if they supported sigmoid.
Understanding the simplex geometry of softmax versus per-expert sigmoid gates is core MoE literacy for 2026 frontier stacks with high k and multi-gate designs.
The multi-select format tests whether you understand gating geometry, not just gating names. Softmax and sigmoid are not interchangeable squashing functions, they encode different assumptions about expert relationships. The correct answers (A, B, E) all describe sigmoid's independence property. Options C and D describe softmax behavior, sometimes inverted, as if they supported sigmoid.
Interviewers asking this question often follow up with "what does Mixtral use?", softmax. Then "when would you deviate?", sigmoid for multi-gate independence. Having both answers ready signals you understand default versus exception.
Senior interviewers reward candidates who connect this mechanism to a concrete deployment or training decision, not only the textbook definition. Close with one number, one failure mode, and one monitoring signal you would track in production.
Softmax routing as zero-sum competition
The simplex constraint. Given router logits z ∈ R^N, softmax weights are p_i = exp(z_i) / Σ_j exp(z_j). Properties:
- p_i > 0 for all i before top-k masking
- Σ_i p_i = 1 always
- Raising one expert's weight lowers others
Top-k selection then zeroes non-selected experts, often followed by renormalization among survivors. But the pre-top-k softmax already forced global competition, experts fought for probability mass even if only k run.
Switch Transformer and Mixtral inherit this tradition.
Simplex geometry. Softmax maps logits to the N-dimensional probability simplex: all weights positive, sum to 1, global competition. Even before top-k masking, raising Expert 5's logit necessarily lowers every other expert's softmax weight. This is the right tool when experts are alternative specialists competing for a fixed routing budget. Switch and Mixtral inherit this tradition because top-1 and top-2 selection naturally pair with competing probability mass.
Training dynamics differ: softmax gradients on router logits couple all experts through the normalization denominator. Sigmoid gradients are per-expert independent. This affects how load-balancing aux losses must be formulated, another reason switching gating functions is not a drop-in replacement.
Situations where this technique stops working.
2–4 min · Everything important, quickly.
Real products, models, and research that use this idea.
- Switch Transformer and Mixtral use softmax-based routing over expert logits in the classic top-k recipe.
- Some frontier MoE models with higher k or multi-gate designs adopt sigmoid or hybrid gating for independent expert activation.
What an interviewer would ask next. Try answering before peeking at the approach.
QHow does auxiliary load-balancing loss interact differently with softmax vs sigmoid gates?
Aux loss often targets fraction of tokens per expert, gating shape changes gradient paths to logits.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Red flags & common mistakes
The phrases that signal junior thinking. Click to expand.
Picking options that describe softmax behavior (global normalization to 1.0) as reasons to choose sigmoid, those are backwards.
60 second bullets to scan on the way to the call.
Softmax simplex competition across N experts
Sigmoid independent per-expert gates
Primary sources. Browse if you want the original framing.
Same topic, related formats. Practice these next.