2 citations · 2 across the 17 of their papers we have counts for
18 papers
CoRe-MoE: Compact Reusable MoE for Continual Multimodal Instruction Tuning
Runze Liu, Naibin Gu, Mingxu Ai +4
Continual multimodal instruction tuning requires multimodal large language models to acquire new task abilities sequentially while preserving previously learned knowledge. LoRA-MoE…
Harnessing Streaming Video in the Wild
Dingyu Yao, Shuhuan Gu, Qingyi Si +8
Vision-Language Models (VLMs) are increasingly required to process unbounded video streams in applications such as video-call assistants, live commentary, and embodied robots. An i…
Learning to Solve, Forgetting to Retain: Correct-Set Turnover in RLVR
Chuanyu Qin, Chenxu Yang, Qingyi Si +3
Reinforcement learning with verifiable rewards (RLVR) improves the ability of large language model, yet headline accuracy gains often conceal a hidden cost: previously solved probl…
Co-Evolving Policy Distillation
Naibin Gu, Chenxu Yang, Qingyi Si +7
RLVR and OPD have become standard paradigms for post-training. We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single mode…
Near-Future Policy Optimization
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning with verifiable rewards (RLVR) has become a core post-training recipe. Introducing suitable off-policy trajectories into on-policy exploration accelerates RL…
EasyVideoR1: Easier RL for Video Understanding
Chuanyu Qin, Chenxu Yang, Qingyi Si +6
Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve i…