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cs.LG2025
REG: A Regularization Optimizer for Robust Training Dynamics
Zehua Liu, Han Wu, Xiaojin Fu +4
Optimizers are crucial for the efficient training of Large Language Models (LLMs). While AdamW is the de facto standard, recent structure-aware optimizers like Muon have emerged, w…
cs.LG2025
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Weizhe Lin, Xing Li, Zhiyuan Yang +7
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning…
cs.LG2025
MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models
Zehua Liu, Han Wu, Ruifeng She +4
Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs) by selectively activating a subset of parameters for each inpu…