1 citations · 1 across the 11 of their papers we have counts for
11 papers
MiniMax Sparse Attention
Xunhao Lai, Weiqi Xu, Yufeng Yang +14
Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointl…
Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling
Yucheng Li, Huiqiang Jiang, Yang Xu +14
Reinforcement learning (RL) has become a key component in modern large language models, yet the rollout stage remains the key bottleneck in RL training pipelines. Although Multi-To…
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
Yang Xu, Zihuai Xu, Hongli Xu +3
Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously deployed task-specific Low-Ran…
Purging the Gray Zone: Latent-Geometric Denoising for Precise Knowledge Boundary Awareness
Hao An, Yibin Lou, Jiayi Guo +1
Large language models (LLMs) often exhibit hallucinations due to their inability to accurately perceive their own knowledge boundaries. Existing abstention fine-tuning methods typi…
Fast and Accurate Probing of In-Training LLMs' Downstream Performances
Zhichen Liu, Tianle Lun, Zhibin Wen +7
The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional ge…
AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Terry Chen, Zhifan Ye, Bing Xu +20
Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evoluti…
LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs
Jianghao Chen, Junhong Wu, Yangyifan Xu +1
Long-context modeling has drawn more and more attention in the area of Large Language Models (LLMs). Continual training with long-context data becomes the de-facto method to equip…
SimulPL: Aligning Human Preferences in Simultaneous Machine Translation
Donglei Yu, Yang Zhao, Jie Zhu +3
Simultaneous Machine Translation (SiMT) generates translations while receiving streaming source inputs. This requires the SiMT model to learn a read/write policy, deciding when to…
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
Junhong Wu, Yang Zhao, Yangyifan Xu +2
Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve tran…
Hit the Sweet Spot! Span-Level Ensemble for Large Language Models
Yangyifan Xu, Jianghao Chen, Junhong Wu +1
Ensembling various LLMs to unlock their complementary potential and leverage their individual strengths is highly valuable. Previous studies typically focus on two main paradigms:…
Bridging the Gap between Different Vocabularies for LLM Ensemble
Yangyifan Xu, Jinliang Lu, Jiajun Zhang
Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discr…