6 citations · 9 across the 7 of their papers we have counts for
7 papers
An Evaluation of Interleaved Instruction Tuning on Semantic Reasoning Performance in an Audio MLLM
Jiawei Liu, Enis Berk Çoban, Zarina Schevchenko +4
Standard training for Multi-modal Large Language Models (MLLMs) involves concatenating non-textual information, like vision or audio, with a text prompt. This approach may not enco…
Policy Gradient Guidance Enables Test Time Control
Jianing Qi, Hao Tang, Zhigang Zhu
We introduce Policy Gradient Guidance (PGG), a simple extension of classifier-free guidance from diffusion models to classical policy gradient methods. PGG augments the policy grad…
Learning to Reason Across Parallel Samples for LLM Reasoning
Jianing Qi, Xi Ye, Hao Tang +2
Scaling test-time compute brings substantial performance gains for large language models (LLMs). By sampling multiple answers and heuristically aggregate their answers (e.g., eithe…
Beyond Semantics: Rediscovering Spatial Awareness in Vision-Language Models
Jianing Qi, Jiawei Liu, Hao Tang +1
Vision Language Models (VLMs) excel at identifying and describing objects but often fail at spatial reasoning. We study why VLMs, such as LLaVA, underutilize spatial cues despite h…
VerifierQ: Enhancing LLM Test Time Compute with Q-Learning-based Verifiers
Jianing Qi, Hao Tang, Zhigang Zhu
Recent advancements in test time compute, particularly through the use of verifier models, have significantly enhanced the reasoning capabilities of Large Language Models (LLMs). T…
GMC: A General Framework of Multi-stage Context Learning and Utilization for Visual Detection Tasks
Xuan Wang, Hao Tang, Zhigang Zhu
Various contextual information has been employed by many approaches for visual detection tasks. However, most of the existing approaches only focus on specific context for specific…