CIPHER-MoE: Balancing Efficiency and Routing Fidelity in Trillion-Scale MoE Training
cs.LG, cs.AI
Submitted: 2026-10-05
Updated: 2026-10-05
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- On the Opportunities and Risks of Foundation Models
- $\phi$-Balancing for Mixture-of-Experts Training
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Training Verifiers to Solve Math Word Problems
- GLM-5: from Vibe Coding to Agentic Engineering
- DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
- ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning
- Scaling Laws for Neural Language Models
- GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
- DeepSeek-V3 Technical Report
- Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
- GPQA: A Graduate-Level Google-Proof Q&A Benchmark
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
- Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
- ORGEval: Graph-Theoretic Evaluation of LLMs in Optimization Modeling
- Instruction-Following Evaluation for Large Language Models
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