C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization
  for Test-Time Expert Re-Mixing
Mixture-of-Experts (MoE) Large Language Models (LLMs) suffer from severely
sub-optimal expert pathways-our study reveals that naive expert selection
learned from pretraining leaves a surprising 10-20% accuracy gap for
improvement. Motivated by this observation, we develop a novel class of
test-time optimization methods to re-weight or "re-mixing" the experts in
different layers jointly for each test sample. Since the test sample's ground
truth is unknown, we propose to optimize a surrogate objective defined by the
sample's "successful neighbors" from a reference set of samples. We introduce
three surrogates and algorithms based on mode-finding, kernel regression, and
the average loss of similar reference samples/tasks. To reduce the cost of
optimizing whole pathways, we apply our algorithms merely to the core experts'
mixing weights in critical layers, which enjoy similar performance but save
significant computation. This leads to "Critical-Layer, Core-Expert,
Collaborative Pathway Optimization (C3PO)". We apply C3PO to two recent MoE
LLMs and examine it on six widely-used benchmarks. It consistently improves the
base model by 7-15% in accuracy and outperforms widely used test-time learning
baselines, e.g., in-context learning and prompt/prefix tuning, by a large
margin. Moreover, C3PO enables MoE LLMs with 1-3B active parameters to
outperform LLMs of 7-9B parameters, hence improving MoE's advantages on
efficiency. Our thorough ablation study further sheds novel insights on
achieving test-time improvement on MoE.
67f88c848178ca61d74e001d
Zhongyang Li
claimed_verified
2025-04-11T07:25:08.340Z
671002fd13203512e7b8f9e3
/avatars/313d8ea313ed300750cfdaaca44fdb6e.svg
Zhongyang Li
user
Lzy01241010
67f88c848178ca61d74e001e
Ziyue Li
67f88c848178ca61d74e001f
Tianyi Zhou
claimed_verified
2025-04-11T07:25:10.558Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
C3PO: Critical-Layer, Core-Expert, Collaborative Pathway Optimization
  for Test-Time Expert Re-Mixing
Mixture-of-Experts (MoE) Large Language Models (LLMs) suffer from severely
sub-optimal expert pathways-our study reveals that naive expert selection
learned from pretraining leaves a surprising 10-20% accuracy gap for
improvement. Motivated by this observation, we develop a novel class of
test-time optimization methods to re-weight or "re-mixing" the experts in
different layers jointly for each test sample. Since the test sample's ground
truth is unknown, we propose to optimize a surrogate objective defined by the
sample's "successful neighbors" from a reference set of samples. We introduce
three surrogates and algorithms based on mode-finding, kernel regression, and
the average loss of similar reference samples/tasks. To reduce the cost of
optimizing whole pathways, we apply our algorithms merely to the core experts'
mixing weights in critical layers, which enjoy similar performance but save
significant computation. This leads to "Critical-Layer, Core-Expert,
Collaborative Pathway Optimization (C3PO)". We apply C3PO to two recent MoE
LLMs and examine it on six widely-used benchmarks. It consistently improves the
base model by 7-15% in accuracy and outperforms widely used test-time learning
baselines, e.g., in-context learning and prompt/prefix tuning, by a large
margin. Moreover, C3PO enables MoE LLMs with 1-3B active parameters to
outperform LLMs of 7-9B parameters, hence improving MoE's advantages on
efficiency. Our thorough ablation study further sheds novel insights on
achieving test-time improvement on MoE.
A novel test-time optimization method, C3PO, improves the accuracy and efficiency of Mixture-of-Experts Large Language Models by re-weighting experts based on surrogate objectives from neighboring samples.
Mixture-of-Experts (MoE) Large Language Models (LLMs) expert selection test-time optimization surrogate objective successful neighbors mode-finding kernel regression average loss core experts critical layers in-context learning prompt/prefix tuning ablation study