1 citations · 1 across the 16 of their papers we have counts for
16 papers
CompassOPD: Cross-Family On-Policy Distillation via Within-Family Likelihood Shifts
Naibin Gu, Qingyi Si, Chenxu Yang +5
On-policy distillation (OPD) provides dense token-level supervision on student-generated trajectories. Although OPD performs strongly when teacher and student belong to the same mo…
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…
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…
Self-Distilled RLVR
Chenxu Yang, Chuanyu Qin, Qingyi Si +7
On-policy distillation (OPD) has become a popular training paradigm in the LLM community. This paradigm selects a larger model as the teacher to provide dense, fine-grained signals…
System 1&2 Synergy via Dynamic Model Interpolation
Chenxu Yang, Qingyi Si, Chong Tian +6
Training a unified language model that adapts between intuitive System 1 and deliberative System 2 remains challenging due to interference between their cognitive modes. Recent stu…
Weights-Rotated Preference Optimization for Large Language Models
Chenxu Yang, Ruipeng Jia, Mingyu Zheng +6
Despite the efficacy of Direct Preference Optimization (DPO) in aligning Large Language Models (LLMs), reward hacking remains a pivotal challenge. This issue emerges when LLMs exce…
DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
Yisu Liu, Chenxing Li, Wanqian Zhang +6
Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and…
Test-time Prompt Intervention
Chenxu Yang, Qingyi Si, Mz Dai +5
Test-time compute has led to remarkable success in the large language model (LLM) community, particularly for complex tasks, where longer chains of thought (CoTs) are generated to…
TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning
Mingyu Zheng, Zhifan Feng, Jia Wang +4
Despite the commendable progress of recent LLM-based data synthesis methods, they face two limitations in generating table instruction tuning data. First, they can not thoroughly e…
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models
Naibin Gu, Peng Fu, Xiyu Liu +3
Parameter-efficient fine-tuning (PEFT) has become a common method for fine-tuning large language models, where a base model can serve multiple users through PEFT module switching.…
Advantageous Parameter Expansion Training Makes Better Large Language Models
Naibin Gu, Yilong Chen, Zhenyu Zhang +7
Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead.…
Dynamic Early Exit in Reasoning Models
Chenxu Yang, Qingyi Si, Yongjie Duan +6
Recent advances in large reasoning language models (LRLMs) rely on test-time scaling, which extends long chain-of-thought (CoT) generation to solve complex tasks. However, overthin…
BeamLoRA: Beam-Constraint Low-Rank Adaptation
Naibin Gu, Zhenyu Zhang, Xiyu Liu +7
Due to the demand for efficient fine-tuning of large language models, Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tunin…
A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles
Siyuan Chen, Qingyi Si, Chenxu Yang +4
The advent of large language models (LLMs) has significantly propelled the advancement of Role-Playing Agents (RPAs). However, current Role-Playing Agents predominantly focus on mi…
Orthogonal Finetuning for Direct Preference Optimization
Chenxu Yang, Ruipeng Jia, Naibin Gu +7
DPO is an effective preference optimization algorithm. However, the DPO-tuned models tend to overfit on the dispreferred samples, manifested as overly long generations lacking dive…