1 citations · 1 across the 6 of their papers we have counts for
7 papers
Multiplex Thinking: Reasoning via Token-wise Branch-and-Merge
Yao Tang, Li Dong, Yaru Hao +3
Large language models often solve complex reasoning tasks more effectively with Chain-of-Thought (CoT), but at the cost of long, low-bandwidth token sequences. Humans, by contrast,…
BitNet Distillation
Xun Wu, Shaohan Huang, Wenhui Wang +4
In this paper, we present BitNet Distillation (BitDistill), a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ter…
Information-Preserving Reformulation of Reasoning Traces for Antidistillation
Jiayu Ding, Lei Cui, Li Dong +2
Recent advances in Large Language Models (LLMs) show that extending the length of reasoning chains significantly improves performance on complex tasks. While revealing these reason…
Reinforcement Pre-Training
Qingxiu Dong, Li Dong, Yao Tang +4
In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling paradigm for large language models and reinforcement learning (RL). Specifically, we reframe next-token…
Rectified Sparse Attention
Yutao Sun, Tianzhu Ye, Li Dong +6
Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignmen…
Scaling Laws of Synthetic Data for Language Models
Zeyu Qin, Qingxiu Dong, Xingxing Zhang +10
Large language models (LLMs) achieve strong performance across diverse tasks, largely driven by high-quality web data used in pre-training. However, recent studies indicate this da…