TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice

Model Description

TC-MoE is a novel Mixture-of-Experts (MoE) architecture that enhances traditional MoE models through expert space expansion. By applying the ternary set {-1, 0, 1} to each original expert, TC-MoE achieves:

• ​9% reduction in activated experts compared to Top-K routing
• ​1.1% average performance gain on language understanding benchmarks
• Flexible efficiency-effectiveness trade-off via reward mechanism

Key innovations:

• 🎯 ​Ternary Expert Expansion: Creates parameter-sharing expert variants (-1, 0, +1) without significant computational overhead
• ⚖️ ​Adaptive Load Balancing: Novel load balance loss for expert workload distribution
• 🎮 ​Reward-Driven Routing: Dynamic control of expert activation ratios

Model Overview

• ​Architecture: Decoder-only transformer based on LLaMA
• ​Pretraining Data:
  • RedPajama (100B tokens)
• ​Model Size:
  • Base (681M/2.3B params)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("stiger1000/TC-MoE")
tokenizer = AutoTokenizer.from_pretrained("stiger1000/TC-MoE")

inputs = tokenizer("The capital of France is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))

Training Details

• Optimizer: AdamW (β₁=0.9, β₂=0.95)
• Learning Rate: 1e-4 with cosine decay
• Batch Size: 4M tokens
• Loss Components:
  • Language Modeling Loss
  • Load Balance Loss (α₁=0.01)
  • Reward Loss (α₂=0.0)

Citation

@inproceedings{yan2025tcmoe,
  title={TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice},
  author={Yan, Shen and Bin, Xingyan and Zhang, Sijun and Wang, Yisen and Lin, Zhouchen},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}

📚 Repository: GitHub