LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
Junhong Wu · Jinliang Lu · Zixuan Ren · Gangqiang Hu · Zhi Wu · Dai Dai · hua wu
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Junhong Wu · Jinliang Lu · Zixuan Ren · Gangqiang Hu · Zhi Wu · Dai Dai · hua wu
Zixuan Ren · Jinliang Lu · Junhong Wu · Yang Zhao · Dai Dai · hua wu · Haifeng Wang · Chengqing Zong
Yixuan Wang · Huang He · Siqi Bao · hua wu · Haifeng Wang · Qingfu Zhu · Wanxiang Che
Yuchen Feng · Zhenyu Zhang · Naibin Gu · Yilong Chen · Peng Fu · Zheng Lin · Shuohuan Wang · YU SUN · hua wu · Weiping Wang · Haifeng Wang
Jiadong Pan · Liang Li · Yuxin Peng · Yu-Ming Tang · Shuohuan Wang · YU SUN · hua wu · Qingming Huang · Haifeng Wang
Yuhao Wang · Ruiyang Ren · Yucheng Wang · Wayne Xin Zhao · Jing Liu · hua wu · Haifeng Wang
Long-form question answering (LFQA) requires open-ended long-form responses that synthesize coherent, factually grounded content from multi-source evidence. This makes reinforcement learning (RL) reward design critical. The reward must be verifiable for faithful grounding and stable optimization....
Shaoning Sun · Mingzhu Cai · Huang He · Bingjin Chen · Siqi Bao · Yujiu Yang · hua wu · Haifeng Wang
Language model families exhibit striking disparity in their capacity to benefit from reinforcement learning: under identical training, models like Qwen achieve substantial gains, while others like Llama yield limited improvements. Complementing data-centric approaches, we reveal that this dispari...
Yilong Chen · Junyuan Shang · Yuchen Feng · Zhenyu Zhang · Naibin Gu · Ziqi Wang · Tingwen Liu · Shuohuan Wang · YU SUN · hua wu · Haifeng Wang
Mixture-of-Experts (MoE) is a cornerstone for scaling LLMs, yet its training dynamics remain poorly understood, often leading to sub-optimal specialization. Moving beyond static routing, we present a systematic study of the MoE lifecycle using Helmholtz Free Energyand Router Entropy. We identify...
Shuaiyi Nie · Dingsiyu · Wenyuan Zhang · Linhao Yu · Tianmeng Yang · Yao Chen · Weichong Yin · YU SUN · hua wu · Tingwen Liu
Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redundant reasoning without performance gains. Existing trajectory-level length penalties often fail to effectively short...
Yao Chen · Yilong Chen · Yinqi Yang · Junyuan Shang · Zhenyu Zhang · Zefeng Zhang · Shuaiyi Nie · Shuohuan Wang · YU SUN · hua wu · Haifeng Wang · Tingwen Liu
Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution.Under this paradigm, the network structure remains static along the training timeline, and additional computational depth is uniformly ass...
Yishu Lei · Shuwei He · Hu Jing · Dan Zhang · Xianlong Luo · Danxiang Zhu · Shikun Feng · Rui Liu · Jingzhou HE · YU SUN · hua wu · Haifeng Wang
Extending the input modality of Large Language Models (LLMs) to the audio domain is essential for achieving comprehensive multimodal perception. However, it is well-known that acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental conte...
Hu Jing · Danxiang Zhu · Xianlong Luo · Dan Zhang · Shuwei He · Yishu Lei · Shikun Feng · Hai-Tao Zheng · Jingzhou HE · YU SUN · hua wu · Haifeng Wang
Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degradation in knowledge and reasoning capabilities. We hypothesize that this limitation stems from the failure of current...
Deqiang Huang · Jingbo Zhou · Xinjiang Lu · an Jiaotong University) · hua wu · Enhong Chen (University of Science and Technology of China &
Yuhao Wang · Ruiyang Ren · Yucheng Wang · Jing Liu · Wayne Xin Zhao · hua wu · Haifeng Wang