4 papers
When LLMs get significantly worse: A statistical approach to detect model degradations
Jonas Kübler, Kailash Budhathoki, Matthäus Kleindessner +4
Minimizing the inference cost and latency of foundation models has become a crucial area of research. Optimization approaches include theoretically lossless methods and others with…
Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning
Zhi Zhang, Zhen Han, Costas Mavromatis +9
Reinforcement learning (RL) plays a central role in large language model (LLM) post-training. Among existing approaches, Group Relative Policy Optimization (GRPO) is widely used, e…
MiCEval: Unveiling Multimodal Chain of Thought's Quality via Image Description and Reasoning Steps
Xiongtao Zhou, Jie He, Lanyu Chen +5
Multimodal Chain of Thought (MCoT) is a popular prompting strategy for improving the performance of multimodal large language models (MLLMs) across a range of complex reasoning tas…
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
Xiongtao Zhou, Jie He, Yuhua Ke +3
Multimodal large language models (MLLMs) fine-tuned with multimodal instruction datasets have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all par…