Skip to content
Preprint

Routing Divergence Is Not Evidence of Behavioral Influence in Same-Weight MoE Self-Distillation

Aug 2026 · 0 citations · 40 references
Computer Science

TL;DR

Two Mixture-of-Experts forward passes can share every weight yet route the same token through different experts, creating a possible blind spot in same-weight self-distillation, where a demonstration-conditioned teacher supervises a query-only student.

Abstract

Two Mixture-of-Experts (MoE) forward passes can share every weight yet route the same token through different experts. This creates a possible blind spot in same-weight self-distillation, where a demonstration-conditioned teacher supervises a query-only student. We study this mismatch in its single-step form, with frozen weights rather than as a proxy for a full training trajectory. An exact blockwise decomposition separates a routing term, which changes gates at fixed content, from a dense-like content term. Across seven open-weight checkpoints and two domains, the routing term spans only $1.6\times$ as a fraction of block output, while its residual-stream exposure spans $3.2\times$. Exposure is ordered by the routed block's share of the residual. Scaling the always-on backbone in two confirmatory models moves exposure monotonically; common-mode controls support a mass-and-coherence mechanism rather than denominator dilution alone. Preregistered PubMedQA patches on three models show that the full routing term moves outputs by less than half the natural context effect and is largely reproduced by matched-norm noise, whereas the content term is strongly direction-specific. Scale and merged-expert probes show that the narrow block-level range is not universal, although exposure remains small at the tested boundaries. Router movement alone is therefore not evidence of behavioral influence: measure exposure first, and use a behavioral intervention when the decision matters.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

A Persistent State for Auditable Mixture-of-Experts Routing

Mixture-of-Experts (MoE) models repeatedly route tokens to sparse subsets of experts, but conventional routers expose no routing-specific record of how cross-layer influences accumulate. We introduce Scratchpad-Augmented Mixture-of-Experts (SA-MoE), which gives each router access to a low-dimensional persistent state t...

Abdurrahman Javat, A. Kazakov · 0 citations
#artificial intelligence Preprint Sep 2026

MOPD-Router: Rethinking Teacher Routing in Multi-Teacher On-Policy Distillation

This work proposes ExpertAlign, a framework that routes supervision over the full teacher pool at each token, without domain labels or training a separate routing model, and demonstrates token-level routing can exploit cross-domain complementary supervision, and reduce exclusive reliance on prompt-level domain assignme...

Tian-Ze Xu, Yan-Zhao Zheng, Zhen-Tao Zhang et al. · 0 citations
#machine learning Preprint Sep 2026

Beyond the Previous Layer: Residual Predictive Structure in Sparse MoE Routing

Using frozen OLMoE and JetMoE models, residual predictive structure in expert-selection trajectories beyond adjacent-layer persistence is identified, identifying residual predictive structure in expert-selection trajectories beyond adjacent-layer persistence.

Hao Li, Yasuyuki Tahara, Y. Sei · 0 citations
#artificial intelligence Preprint Sep 2026

From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts

Test-time scaling produces cohorts of reasoning rollouts, yet there is no standard label-free account of how their internal computation reorganizes as inference unfolds. We introduce routing effective rank deff, the entropy-effective dimensionality of a cross-rollout graph built from MoE expert-routing similarity. Acro...

Kang Chen, Si-Han Zhao, Yi-Xin Cao et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Evidence for Shared Routing Geometry and Dynamics in Sparse Mixture-of-Experts

Evidence is provided that routing-relevant states across layers share a common geometric structure that is obscured by layer-specific coordinate systems, and whether this shared dynamics is specific to routing or simply reflects the smooth evolution of hidden representations.

Kirill Labzin, Stepan Kulibaba, Artem Dzhalilov et al. · 1 citation
#artificial intelligence Preprint Sep 2026

Router Prior Bias: Preserving Base Routing Structure in MoE Post-Training

Mixture-of-Experts (MoE) pretraining relies on an auxiliary load-balancing loss (LBL) to drive per-expert utilization toward uniformity. Post-training inherits a different situation: the base router already encodes non-uniform expert co-activation structure, which a re-imposed uniformity objective flattens away. We sho...

Jaedeok Lee, Keonwoo Kim, Dongyoon Han et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.