1 citations · 1 across the 12 of their papers we have counts for
12 papers
Reasoning Error from Known Fact: Step-Level Self-Consistency Group Relative Policy Optimization for LLMs
Xiaomeng Hu, Jiaqi Hu, Hao Chen +4
With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge, but can also solve complex pro…
FLaG: Fine-Grained Latent Grouping for Hallucination Detection
Wentao Ye, Liyao Li, Zhiqing Xiao +6
Hallucinations in large language models (LLMs) arise from heterogeneous failure mechanisms, making reliable detection difficult for any single global uncertainty score. In this wor…
Training-Free Quantum Generative Paradigm via Local Parent Hamiltonians
Shu Tian, Jiaqi Hu, Rebing Wu +1
We propose a training-free quantum generative paradigm, which is fundamentally different from current generative models, which demand substantial computational power, face practica…
Supervised Fine-Tuning Needs to Unlock the Potential of Token Priority
Zhanming Shen, Zeyu Qin, Jiaqi Hu +7
The transition from fitting empirical data to achieving true human utility is fundamentally constrained by a granularity mismatch, where fine-grained autoregressive generation is o…
Training-Trajectory-Aware Token Selection
Zhanming Shen, Jiaqi Hu, Zeyu Qin +7
Efficient distillation is a key pathway for converting expensive reasoning capability into deployable efficiency, yet in the frontier regime where the student already has strong re…
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…
An Invariant Latent Space Perspective on Language Model Inversion
Wentao Ye, Jiaqi Hu, Haobo Wang +7
Language model inversion (LMI), i.e., recovering hidden prompts from outputs, emerges as a concrete threat to user privacy and system security. We recast LMI as reusing the LLM's o…
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…
Merge-of-Thought Distillation
Zhanming Shen, Zeyu Qin, Zenan Huang +6
Efficient reasoning distillation for long chain-of-thought (CoT) models is increasingly constrained by the assumption of a single oracle teacher, despite the practical availability…
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…
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…
Data Contamination Calibration for Black-box LLMs
Wentao Ye, Jiaqi Hu, Liyao Li +3
The rapid advancements of Large Language Models (LLMs) tightly associate with the expansion of the training data size. However, the unchecked ultra-large-scale training sets introd…