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cs.LG2026
RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanism
Mengyang Sun, Maochuan Dou, Tao Feng +5
While Large Language Models (LLMs) are commonly fine-tuned to handle domain-specific tasks before being applied to vertical applications, adapting them to complex scenarios with di…
cs.LG2025★ 1 cited
TDRM: Smooth Reward Models with Temporal Difference for LLM RL and Inference
Dan Zhang, Min Cai, Jonathan Light +3
Reward models are central to both reinforcement learning (RL) with language models and inference-time verification. However, existing reward models often lack temporal consistency,…
cs.LG2025
A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
Mengyang Sun, Yihao Wang, Tao Feng +3
In order to streamline the fine-tuning of foundation models, Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domai…