3 papers
cs.CV2026
5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
Yifan Zhu, Can Lin, Hangjie Yuan +4
Parameter-Efficient Fine-Tuning (PEFT) methods provide a streamlined and efficient tool for adapting large models to domain-specific multimodal downstream tasks. Although these met…
cs.AI2026
Can the Environment Speak for Itself? -GRPO: A Turn-Trajectory Group Relative Policy Optimization for Caregiver Agents
Yutong Song, Jiang Wu, Pengfei Zhang +4
Optimizing large language models (LLMs) for long-horizon caregiver agents requires balancing delayed task objectives with immediate environment dynamics, such as patient distress a…
cs.LG2026
Adaptive Loss Balancing for Noise-Robust GRPO in Generative Recommendation
Kewei Xu, Junbo Qi, Yanyan Zou +3
Reinforcement learning (RL) presents a promising avenue for enhancing generative recommendation beyond supervised imitation, leveraging reward signals to guide policy improvement.…