26 papers
Why Does Feedback-Augmented Self-Distillation Fail to Improve Retrieval-Interleaved Search Agents?
Fan Yang, Rui Meng, Yuxin Wen
On-policy self-distillation (OPSD) offers a promising approach for training large language models without relying on a separate teacher model. However, its effectiveness on complex…
What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework
Hongze Wang, Boyang Sun, Jiaxu Xing +5
Object-Goal Navigation (ObjectNav) is a key capability for deploying mobile robots in everyday environments such as homes, schools, and workplaces. In this task, an agent must loca…
Owen-Shapley Policy Optimization: A Principled RL Algorithm for Generative Search LLMs
Abhijnan Nath, Alireza Bagheri Garakani, Tianchen Zhou +3
Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewar…
MRD: Multi-resolution Retrieval-Detection Fusion for High-Resolution Image Understanding
Fan Yang, Xingping Dong, Xin Yu +3
Understanding high-resolution (HR) images remains a critical challenge for multimodal large language models (MLLMs). Recent approaches leverage vision-based retrieval-augmented gen…
Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
Yueqi Song, Ketan Ramaneti, Zaid Sheikh +18
Public research results on large-scale supervised finetuning of AI agents remain relatively rare, since the collection of agent training data presents unique challenges. In this wo…
TraceVision: Trajectory-Aware Vision-Language Model for Human-Like Spatial Understanding
Fan Yang, Shurong Zheng, Hongyin Zhao +5
Recent Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities in image understanding and natural language generation. However, current approaches focus predominan…