Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
A Comparative Theoretical Analysis of Entropy Control Methods in Reinforcement Learning
Ming Lei, Christophe Baehr
Reinforcement learning (RL) has become a key approach for enhancing reasoning in large language models (LLMs), yet scalable training is often hindered by the rapid collapse of poli…
cs.LG2026
FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
Changyu Li, Shuanghong Huang, Jiashen Liu +5
Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data. However, in mobile deployments, the traini…