6 citations · 10 across the 21 of their papers we have counts for
22 papers
VibeVoice-ASR-BitNet Technical Report
Songchen Xu, Ting Song, Shaohan Huang +10
We present VibeVoice-ASR-BitNet, a compressed variant of VibeVoice-ASR optimized for real-time inference on edge CPUs. We apply heterogeneous quantization tailored to the computati…
BitNet Text Embeddings
Zhen Li, Xin Huang, Liang Wang +8
LLM-based text embedders have substantially improved retrieval and semantic representation quality, but their deployment remains costly: large backbone models slow down embedding i…
LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
Tianzhu Ye, Li Dong, Guanheng Chen +4
Reinforcement learning (RL) on open-ended tasks compresses an LLM's rubric-based evaluation into a scalar reward, discarding rich textual feedback and conflating responses with dis…
On-Policy Context Distillation for Language Models
Tianzhu Ye, Li Dong, Xun Wu +2
Context distillation enables language models to internalize in-context knowledge into their parameters. In our work, we propose On-Policy Context Distillation (OPCD), a framework t…
SlideSparse: Fast and Flexible (2N-2):2N Structured Sparsity
Hanyong Shao, Yingbo Hao, Ting Song +10
NVIDIA's 2:4 Sparse Tensor Cores deliver 2x throughput but demand strict 50% pruning -- a ratio that collapses LLM reasoning accuracy (Qwen3: 54% to 15%). Milder patter…
Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity
Di Zhang, Xun Wu, Shaohan Huang +9
Semi-structured N:M sparsity and low-bit quantization (e.g., 1.58-bit BitNet) are two promising approaches for improving the efficiency of large language models (LLMs), yet they ha…
Online Experiential Learning for Language Models
Tianzhu Ye, Li Dong, Qingxiu Dong +3
The prevailing paradigm for improving large language models relies on offline training with human annotations or simulated environments, leaving the rich experience accumulated dur…
Black-Box On-Policy Distillation of Large Language Models
Tianzhu Ye, Li Dong, Zewen Chi +3
Black-box distillation creates student large language models (LLMs) by learning from a proprietary teacher model's text outputs alone, without access to its internal logits or para…
The Era of Agentic Organization: Learning to Organize with Language Models
Zewen Chi, Li Dong, Qingxiu Dong +4
We envision a new era of AI, termed agentic organization, where agents solve complex problems by working collaboratively and concurrently, enabling outcomes beyond individual intel…
Code Aesthetics with Agentic Reward Feedback
Bang Xiao, Lingjie Jiang, Shaohan Huang +5
Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug…
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…
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts
Di Zhang, Xun Wu, Shaohan Huang +6
Recent advances in reinforcement learning (RL) have substantially improved the training of large-scale language models, leading to significant gains in generation quality and reaso…
VisCodex: Unified Multimodal Code Generation via Merging Vision and Coding Models
Lingjie Jiang, Shaohan Huang, Xun Wu +3
Multimodal large language models (MLLMs) have significantly advanced the integration of visual and textual understanding. However, their ability to generate code from multimodal in…
Geometric-Mean Policy Optimization
Yuzhong Zhao, Yue Liu, Junpeng Liu +9
Group Relative Policy Optimization (GRPO) has significantly enhanced the reasoning capability of large language models by optimizing the arithmetic mean of token-level rewards. Unf…
On-Policy RL with Optimal Reward Baseline
Yaru Hao, Li Dong, Xun Wu +3
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement…
Think Only When You Need with Large Hybrid-Reasoning Models
Lingjie Jiang, Xun Wu, Shaohan Huang +7
Recent Large Reasoning Models (LRMs) have shown substantially improved reasoning capabilities over traditional Large Language Models (LLMs) by incorporating extended thinking proce…
Reward Reasoning Model
Jiaxin Guo, Zewen Chi, Li Dong +4
Reward models play a critical role in guiding large language models toward outputs that align with human expectations. However, an open challenge remains in effectively utilizing t…
MH-MoE: Multi-Head Mixture-of-Experts
Shaohan Huang, Xun Wu, Shuming Ma +1
Multi-Head Mixture-of-Experts (MH-MoE) demonstrates superior performance by using the multi-head mechanism to collectively attend to information from various representation spaces…
Textual Aesthetics in Large Language Models
Lingjie Jiang, Shaohan Huang, Xun Wu +1
Image aesthetics is a crucial metric in the field of image generation. However, textual aesthetics has not been sufficiently explored. With the widespread application of large lang…
Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation
Xun Wu, Shaohan Huang, Furu Wei
Recent studies have demonstrated the exceptional potentials of leveraging human preference datasets to refine text-to-image generative models, enhancing the alignment between gener…
Multi-Head Mixture-of-Experts
Xun Wu, Shaohan Huang, Wenhui Wang +1
Sparse Mixtures of Experts (SMoE) scales model capacity without significant increases in training and inference costs, but exhibits the following two issues: (1) Low expert activat…
Mixture of LoRA Experts
Xun Wu, Shaohan Huang, Furu Wei
LoRA has gained widespread acceptance in the fine-tuning of large pre-trained models to cater to a diverse array of downstream tasks, showcasing notable effectiveness and efficienc…