1 citations · 1 across the 17 of their papers we have counts for
18 papers
Reducing Pretraining-Generation Mismatch in Diffusion Language Models
Xiaocheng Lu, Huabin Liu, Song Guo +1
Diffusion language models (dLLMs) generate text through iterative denoising, allowing multiple tokens to be predicted in parallel. However, pretraining may mask tokens throughout a…
Quantization Degradation in Large Language Models: A Signal-Noise Perspective
Chenxi Zhou, Pengfei Cao, Jinyu Ye +5
Post-training quantization reduces the deployment cost of large language models, yet how severely a quantized model degrades is not determined by bit-width alone. We systematically…
LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models
Fengqi Zhu, Shaoxuan Xu, Jingyang Ou +11
Diffusion language models (dLLMs) offer an alternative to autoregressive (AR) language modeling, yet the scaling behavior of Mixture-of-Experts (MoE) dLLMs remains poorly understoo…
WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
Juyong Jiang, Chenglin Cai, Chansung Park +4
While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain high…
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
Chenxi Zhou, Pengfei Cao, Jiang Li +4
Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded as an optimal trade-off, redu…
TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models
Chansung Park, Juyong Jiang, Fan Wang +4
Large Language Models (LLMs) are changing the coding paradigm, known as vibe coding, yet synthesizing algorithmically sophisticated and robust code still remains a critical challen…
LLaDA2.1: Speeding Up Text Diffusion via Token Editing
Tiwei Bie, Maosong Cao, Xiang Cao +47
While LLaDA2.0 showcased the scaling potential of 100B-level block-diffusion models and their inherent parallelization, the delicate equilibrium between decoding speed and generati…
LLaDA2.0: Scaling Up Diffusion Language Models to 100B
Tiwei Bie, Maosong Cao, Kun Chen +28
This paper presents LLaDA2.0 -- a tuple of discrete diffusion large language models (dLLM) scaling up to 100B total parameters through systematic conversion from auto-regressive (A…
Knocking-Heads Attention
Zhanchao Zhou, Xiaodong Chen, Haoxing Chen +2
Multi-head attention (MHA) has become the cornerstone of modern large language models, enhancing representational capacity through parallel attention heads. However, increasing the…
dInfer: An Efficient Inference Framework for Diffusion Language Models
Yuxin Ma, Lun Du, Lanning Wei +20
Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs, leveraging denoising-based generation to enable inherent parallel…
DND: Boosting Large Language Models with Dynamic Nested Depth
Tieyuan Chen, Xiaodong Chen, Haoxing Chen +3
We introduce Dynamic Nested Depth (DND), a novel method that improves performance for off-the-shelf LLMs by selecting critical tokens to reprocess in a nested depth manner. Specifi…
CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit
Kangyu Wang, Zhiyun Jiang, Haibo Feng +5
Diffusion large language models (dLLMs) generate text through iterative denoising. In commonly adopted parallel decoding schemes, each step confirms only high-confidence positions…
LLaDA-MoE: A Sparse MoE Diffusion Language Model
Fengqi Zhu, Zebin You, Yipeng Xing +23
We introduce LLaDA-MoE, a large language diffusion model with the Mixture-of-Experts (MoE) architecture, trained from scratch on approximately 20T tokens. LLaDA-MoE achieves compet…
MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks
Mingsong Li, Lin Liu, Hongjun Wang +7
Current instruction-based image editing (IBIE) methods struggle with challenging editing tasks, as both editing types and sample counts of existing datasets are limited. Moreover,…
Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Kangyu Wang, Hongliang He, Lin Liu +3
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse sc…
Reinforcement Learning with Rubric Anchors
Zenan Huang, Yihong Zhuang, Guoshan Lu +18
Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series…
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Haoyuan Wu, Haoxing Chen, Xiaodong Chen +10
The Mixture of Experts (MoE) architecture is a cornerstone of modern state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate scalability by enabling sparse para…
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
Chenxi Zhou, Pengfei Cao, Jiang Li +4
Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws primarily focus on general performance…