MONA integrates Nesterov acceleration into Muon's orthogonalization framework, reporting better convergence than Muon and AdamW on MoE models up to 68B parameters trained on 1T tokens and SOTA fine-tuning results.
Shortcut-connected expert parallelism for accelerating mixture-of-experts
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Chameleon provides adaptive fault tolerance for distributed training by real-time selection of optimal recovery policies via a unified performance model, demonstrated with low overhead on a 32-card cluster.
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
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MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
MONA integrates Nesterov acceleration into Muon's orthogonalization framework, reporting better convergence than Muon and AdamW on MoE models up to 68B parameters trained on 1T tokens and SOTA fine-tuning results.
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Chameleon: Adaptive Fault Tolerance for Distributed Training via Real-time Policy Selection
Chameleon provides adaptive fault tolerance for distributed training by real-time selection of optimal recovery policies via a unified performance model, demonstrated with low overhead on a 32-card cluster.
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A Survey on Large Language Models for Code Generation
A systematic literature review that organizes recent work on LLMs for code generation into a taxonomy covering data curation, model advances, evaluations, ethics, environmental impact, and applications, with benchmark comparisons.
- MACS: Modality-Aware Capacity Scaling for Efficient Multimodal MoE Inference