14 papers
VisNec: Measuring and Leveraging Visual Necessity for Multimodal Instruction Tuning
Mingkang Dong, Hongyi Cai, Jie Li +4
The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely require visual reasoning. However, existi…
3SPO: State-Score-Supervised Policy Optimization for LLM Agents
Yu Han, Kailing Li, Yang Jiao +4
Training large language models (LLMs) as autonomous agents via reinforcement learning (RL) has enabled frontier models to achieve superhuman performance in long-horizon tasks. Howe…
When RL Fails after SFT: Rejuvenating Model Plasticity for Robust SFT-to-RL Handoff
Runze Liu, Jiashun Liu, Xu Wan +2
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training. SFT is expected to provide a usefu…
Are Full Rollouts Necessary for On-Policy Distillation?
Yaocheng Zhang, Jiajun Chai, Yuqian Fu +7
On-policy distillation (OPD) provides dense teacher feedback along student-generated rollouts rather than fixed teacher traces and has emerged as a promising post-training paradigm…
A Shared Valence Axis Across Modern LLMs and Human EEG: The Saturation Regularity
Yousef A. Radwan, Xuhui Liu, Kilichbek Haydarov +2
Large language models (LLMs) have emerged as powerful representation learners whose internal features increasingly align with human cognition. We study whether modern LLMs can serv…
Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation
Kailing Li, Tianwen Qian, Lijin Yang +4
Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions. Despite recent progress with vision-la…