1 citations · 1 across the 19 of their papers we have counts for
15 papers
Experience Funnel: A State-Policy Alternating Loop for Self-Evolving Agents
Wenbo Gao, Zhaomou Song, Zhiyuan Ji +7
Autonomous agents powered by large language models (LLMs) continuously accumulate experience through interaction, creating an opportunity to improve future behavior through self-ev…
Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs
Tanzila Rahman, Mehran Taghian Jazi, Yunke Peng +10
Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precis…
HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
Hei Yi Mak, Shadan Golestan, Hoang Le +10
We present, to our knowledge, the first end-to-end FP4 RL post-training, in which both the rollout and training policies, including their forward and backward passes, operate at 4-…
Stable FP4 Training via Transposition-Invariant Block Quantization
Mehdi Rahimifar, Amin Darabi, Mehran Taghian Jazi +6
Reducing training precision is a key lever for improving the e ciency of large language model (LLM) training, but pushing beyond FP8 to 4-bit oating point (FP4) remains challenging…
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
Sijie Wang, Zhengyu Qing, Zhiqiang Tan +6
Reinforcement learning (RL) has become a dominant post-training paradigm, driving the emergence of high-performance RL systems such as veRL for autoregressive large language models…
GNMR: Runtime Stability Control for Low-Precision Large Language Model Training
Boao Kong, Weichen Jia, Engao Zhang +6
Training stability is a key bottleneck in low-precision language model training: efficient low-cost paths can still produce short-lived numerical risks at a small set of operators.…
TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation
Huichao Chai, Zhixin Wu, Xuemiao Li +8
Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-based models, exhibiting scali…
HiFloat4 Format for Language Model Pre-training on Ascend NPUs
Mehran Taghian, Yunke Peng, Xing Huang +22
Training large foundation models at low numerical precision is one of the most promising directions for reducing the compute and memory cost of modern AI. Recent 4-bit floating-poi…
HOTDISK. Finding Massive Protostellar Disks with Water and Refractory Molecular Species
Kai Yang, Yichen Zhang, Kei E. I. Tanaka +16
We present high-angular-resolution (, au) ALMA Band~6 observations from the HOTDISK project (Hot-Origin Tracer survey of DISKs of massive pro…
AgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents
Wenbo Gao, Renxi Liu, Xian Wang +8
Autonomous agents powered by large language models (LLMs) perform complex tasks through long-horizon reasoning and tool interaction, where a fundamental trade-off arises between ex…
EPD-Serve: A Flexible Multimodal EPD Disaggregation Inference Serving System On Ascend
Fan Bai, Pai Peng, Zhengzhi Tang +8
With the widespread adoption of large multimodal models, efficient inference across text, image, audio, and video modalities has become critical. However, existing multimodal infer…
TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Weizhe Lin, Xing Li, Zhiyuan Yang +7
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning…
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
Yehui Tang, Xiaosong Li, Fangcheng Liu +19
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because on…
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
Yehui Tang, Yichun Yin, Yaoyuan Wang +71
Sparse large language models (LLMs) with Mixture of Experts (MoE) and close to a trillion parameters are dominating the realm of most capable language models. However, the massive…
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Yichun Yin, Wenyong Huang, Kaikai Song +49
We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field…