8 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…