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Xiaozhe Ren

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

BitDP: Ultra-low-bit Communication for Data Parallelism in LLM Training

Xiaozhe Ren
Qiong Luo

Training large language models (LLMs) with billions of parameters on trillion-token datasets requires distributed data parallelism at increasingly large scales, where gradient synchronization becomes a communication bottleneck, especially in bandwidth-constrained environments. Although gradient quantization presents a promising solution, it faces two key challenges: maintaining training stability and accuracy for transformer architectures and adapting to modern distributed communication systems. In this paper, we propose BitDP, an ultra-low-bit gradient quantization system that reduces communication costs by up to 32× while preserving model accuracy with less than 1% performance degradation. Our approach achieves numerical stability for large transformer models and seamlessly integrates with existing infrastructures. We evaluate BitDP's effectiveness across various LLM sizes, architectures and optimizers. The results demonstrate significant training efficiency improvements while maintaining convergence quality, establishing BitDP as a scalable and reliable solution for real-world LLM training at industrial scales.

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NeurIPS Conference 2025 Conference Paper

DeepDiver: Adaptive Web-Search Intensity Scaling via Reinforcement Learning

Wenxuan Shi
Haochen Tan
Chuqiao Kuang
Xiaoguang Li
Hanting Chen
Xiaozhe Ren
Yasheng Wang
Lu Hou

Information seeking demands iterative evidence gathering and reflective reasoning, yet large language models (LLMs) still struggle with it in open-web question answering. Existing prompting and supervised fine-tuning (SFT) methods remain fixed by prompt rules or training corpora, and are usually benchmarked only on well-structured wiki sources, limiting real-world adaptability. We introduce $\textbf{WebPuzzle}$, a 24k-sample training and 275-sample test benchmark that evaluates information seeking on the live internet, across both wiki and open-domain queries. Leveraging 7k WebPuzzle instances, we develop $\textbf{DeepDiver}$, a reinforcement-learning (RL) framework that cultivates $\textbf{Search Intensity Scaling (SIS)}$—an emergent ability to escalate search frequency and depth instead of settling on overconfident, under-evidenced answers. With SIS, Qwen2. 5-7B-Instruct and Pangu-7B-Reasoner attain performance on real-web tasks comparable to the 671B-parameter DeepSeek-R1. We detail DeepDiver’s curriculum from cold-start SFT to a well designed RL procedure, and show that its seeking policy generalized from closed-ended queries to open-ended generation such as long-form writing. Our results advance adaptive information seeking in LLMs and provide a rigorous benchmark for future work.

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ICML Conference 2025 Conference Paper

SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

Guoxuan Chen
Han Shi
Jiawei Li
Yihang Gao
Xiaozhe Ren
Yimeng Chen
Xin Jiang
Zhenguo Li

Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless separator tokens (i. e. , punctuations) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM’s effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.

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NeurIPS Conference 2024 Conference Paper

DAPE: Data-Adaptive Positional Encoding for Length Extrapolation

Chuanyang Zheng
Yihang Gao
Han Shi
Minbin Huang
Jingyao Li
Jing Xiong
Xiaozhe Ren
Michael Ng

Positional encoding plays a crucial role in transformers, significantly impact- ing model performance and length generalization. Prior research has introduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences. However, both APE and RPE remain fixed after model training regardless of input data, limiting their adaptability and flexibility. Hence, we expect that the desired positional encoding should be data-adaptive and can be dynamically adjusted with the given attention. In this paper, we propose a Data-Adaptive Positional Encoding (DAPE) method, which dynamically and semantically adjusts based on input context and learned fixed priors. Experimental validation on real-world datasets (Arxiv, Books3, and CHE) demonstrates that DAPE enhances model performances in terms of trained length and length generalization, where the improvements are statistically significant. The model visualization suggests that our model can keep both local and anti-local information. Finally, we successfully train the model on sequence length 128 and achieve better performance at evaluation sequence length 8192, compared with other static positional encoding methods, revealing the benefit of the adaptive positional encoding method.

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ICML Conference 2023 Conference Paper

A Study on Transformer Configuration and Training Objective

Fuzhao Xue
Jianghai Chen
Aixin Sun
Xiaozhe Ren
Zangwei Zheng
Xiaoxin He
Yongming Chen
Xin Jiang 0002

Transformer-based models have delivered impressive results on many tasks, particularly vision and language tasks. In many model training situations, conventional configurations are often adopted. For example, we usually set the base model with hidden size (i. e. model width) to be 768 and the number of transformer layers (i. e. model depth) to be 12. In this paper, we revisit these conventional configurations by studying the the relationship between transformer configuration and training objective. We show that the optimal transformer configuration is closely related to the training objective. Specifically, compared with the simple classification objective, the masked autoencoder is effective in alleviating the over-smoothing issue in deep transformer training. Based on this finding, we propose “Bamboo”, a notion of using deeper and narrower transformer configurations, for masked autoencoder training. On ImageNet, with such a simple change in configuration, the re-designed Base-level transformer achieves 84. 2% top-1 accuracy and outperforms SoTA models like MAE by $0. 9%$. On language tasks, re-designed model outperforms BERT with the default setting by 1. 1 points on average, on GLUE benchmark with 8 datasets.

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NeurIPS Conference 2023 Conference Paper

Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline

Zangwei Zheng
Xiaozhe Ren
Fuzhao Xue
Yang Luo
Xin Jiang
Yang You

Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. However, the inference process for LLMs comes with significant computational costs. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs. Our approach begins by tapping into the potential of LLMs to accurately perceive and predict the response length with minimal overhead. By leveraging this information, we introduce an efficient sequence scheduling technique that groups queries with similar response lengths into micro-batches. We evaluate our approach on real-world instruction datasets using the LLaMA-based model, and our results demonstrate an impressive 86% improvement in inference throughput without compromising effectiveness. Notably, our method is orthogonal to other inference acceleration techniques, making it a valuable addition to many existing toolkits (e. g. , FlashAttention, Quantization) for LLM inference.

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AAAI Conference 2022 Conference Paper

AutoBERT-Zero: Evolving BERT Backbone from Scratch

Jiahui Gao
Hang Xu
Han Shi
Xiaozhe Ren
Philip L. H. Yu
Xiaodan Liang
Xin Jiang
Zhenguo Li

Transformer-based pre-trained language models like BERT and its variants have recently achieved promising performance in various natural language processing (NLP) tasks. However, the conventional paradigm constructs the backbone by purely stacking the manually designed global selfattention layers, introducing inductive bias and thus leads to sub-optimal. In this work, we make the first attempt to automatically discover novel pre-trained language model (PLM) backbone on a flexible search space containing the most fundamental operations from scratch. Specifically, we propose a well-designed search space which (i) contains primitive math operations in the intra-layer level to explore novel attention structures, and (ii) leverages convolution blocks to be the supplementary for attentions in the inter-layer level to better learn local dependency. To enhance the efficiency for finding promising architectures, we propose an Operation- Priority Neural Architecture Search (OP-NAS) algorithm, which optimizes both the search algorithm and evaluation of candidate models. Specifically, we propose Operation- Priority (OP) evolution strategy to facilitate model search via balancing exploration and exploitation. Furthermore, we design a Bi-branch Weight-Sharing (BIWS) training strategy for fast model evaluation. Extensive experiments show that the searched architecture (named AutoBERT-Zero) significantly outperforms BERT and its variants of different model capacities in various downstream tasks, proving the architecture’s transfer and scaling abilities. Remarkably, AutoBERT-Zerobase outperforms RoBERTa-base (using much more data) and BERT-large (with much larger model size) by 2. 4 and 1. 4 higher score on GLUE test set.

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ICML Conference 2021 Conference Paper

SparseBERT: Rethinking the Importance Analysis in Self-attention

Han Shi
Jiahui Gao
Xiaozhe Ren
Hang Xu 0004
Xiaodan Liang
Zhenguo Li
James T. Kwok

Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attention mechanism, a direct method is to visualize the attention map of a pre-trained model. Based on the patterns observed, a series of efficient Transformers with different sparse attention masks have been proposed. From a theoretical perspective, universal approximability of Transformer-based models is also recently proved. However, the above understanding and analysis of self-attention is based on a pre-trained model. To rethink the importance analysis in self-attention, we study the significance of different positions in attention matrix during pre-training. A surprising result is that diagonal elements in the attention map are the least important compared with other attention positions. We provide a proof showing that these diagonal elements can indeed be removed without deteriorating model performance. Furthermore, we propose a Differentiable Attention Mask (DAM) algorithm, which further guides the design of the SparseBERT. Extensive experiments verify our interesting findings and illustrate the effect of the proposed algorithm.

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