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Huazhong Yang

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17 papers

2 author rows

ICLR Conference 2025 Conference Paper

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better

Enshu Liu
Junyi Zhu 0002
Zinan Lin 0001
Xuefei Ning
Shuaiqi Wang
Matthew B. Blaschko
Sergey Yekhanin
Shengen Yan

Diffusion Models (DM) and Consistency Models (CM) are two types of popular generative models with good generation quality on various tasks. When training DM and CM, intermediate weight checkpoints are not fully utilized and only the last converged checkpoint is used. In this work, we find proper checkpoint merging can significantly improve the training convergence and final performance. Specifically, we propose LCSC, a simple but effective and efficient method to enhance the performance of DM and CM, by combining checkpoints along the training trajectory with coefficients deduced from evolutionary search. We demonstrate the value of LCSC through two use cases: (a) Reducing training cost. With LCSC, we only need to train DM/CM with fewer number of iterations and/or lower batch sizes to obtain comparable sample quality with the fully trained model. For example, LCSC achieves considerable training speedups for CM (23$\times$ on CIFAR-10 and 15$\times$ on ImageNet-64). (b) Enhancing pre-trained models. When full training is already done, LCSC can further improve the generation quality or efficiency of the final converged models. For example, LCSC achieves better FID using 1 number of function evaluation (NFE) than the base model with 2 NFE on consistency distillation, and decreases the NFE of DM from 15 to 9 while maintaining the generation quality. Applying LCSC to large text-to-image models, we also observe clearly enhanced generation quality.

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NeurIPS Conference 2025 Conference Paper

R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing

Tianyu Fu
Yi Ge
Yichen You
Enshu Liu
Zhihang Yuan
Guohao Dai
Shengen Yan
Huazhong Yang

Large Language Models (LLMs) achieve impressive reasoning capabilities at the cost of substantial inference overhead, posing substantial deployment challenges. Although distilled Small Language Models (SLMs) significantly enhance efficiency, their performance suffers as they fail to follow LLMs' reasoning paths. Luckily, we reveal that only a small fraction of tokens genuinely diverge reasoning paths between LLMs and SLMs. Most generated tokens are either identical or exhibit neutral differences, such as minor variations in abbreviations or expressions. Leveraging this insight, we introduce Roads to Rome (R2R), a neural token router that selectively utilizes LLMs only for these critical, path-divergent tokens, while leaving the majority of token generation to the SLM. We also develop an automatic data generation pipeline that identifies divergent tokens and generates token-level routing labels to train the lightweight router. We apply R2R to combine R1-1. 5B and R1-32B models from the DeepSeek family, and evaluate on challenging math, coding, and QA benchmarks. With an average activated parameter size of 5. 6B, R2R surpasses the average accuracy of R1-7B by 1. 6×, outperforming even the R1-14B model. Compared to R1-32B, it delivers a 2. 8× wall-clock speedup with comparable performance, advancing the Pareto frontier of test-time scaling efficiency.

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ICLR Conference 2025 Conference Paper

ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

Tianchen Zhao
Tongcheng Fang
Haofeng Huang
Rui Wan
Widyadewi Soedarmadji
Enshu Liu
Shiyao Li
Zinan Lin 0001

Diffusion transformers have demonstrated remarkable performance in visual generation tasks, such as generating realistic images or videos based on textual instructions. However, larger model sizes and multi-frame processing for video generation lead to increased computational and memory costs, posing challenges for practical deployment on edge devices. Post-Training Quantization (PTQ) is an effective method for reducing memory costs and computational complexity. When quantizing diffusion transformers, we find that existing quantization methods face challenges when applied to text-to-image and video tasks. To address these challenges, we begin by systematically analyzing the source of quantization error and conclude with the unique challenges posed by DiT quantization. Accordingly, we design an improved quantization scheme: ViDiT-Q (**V**ideo \& **I**mage **Di**ffusion **T**ransformer **Q**uantization), tailored specifically for DiT models. We validate the effectiveness of ViDiT-Q across a variety of text-to-image and video models, achieving W8A8 and W4A8 with negligible degradation in visual quality and metrics. Additionally, we implement efficient GPU kernels to achieve practical 2-2.5x memory optimization and a 1.4-1.7x end-to-end latency speedup.

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ICLR Conference 2024 Conference Paper

A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models

Enshu Liu
Xuefei Ning
Huazhong Yang
Yu Wang 0002

Recent years have witnessed the rapid progress and broad application of diffusion probabilistic models (DPMs). Sampling from DPMs can be viewed as solving an ordinary differential equation (ODE). Despite the promising performance, the generation of DPMs usually consumes much time due to the large number of function evaluations (NFE). Though recent works have accelerated the sampling to around 20 steps with high-order solvers, the sample quality with less than 10 NFE can still be improved. In this paper, we propose a unified sampling framework (USF) to study the optional strategies for solver. Under this framework, we further reveal that taking different solving strategies at different timesteps may help further decrease the truncation error, and a carefully designed \emph{solver schedule} has the potential to improve the sample quality by a large margin. Therefore, we propose a new sampling framework based on the exponential integral formulation that allows free choices of solver strategy at each step and design specific decisions for the framework. Moreover, we propose $S^3$, a predictor-based search method that automatically optimizes the solver schedule to get a better time-quality trade-off of sampling. We demonstrate that $S^3$ can find outstanding solver schedules which outperform the state-of-the-art sampling methods on CIFAR-10, CelebA, ImageNet-64, and LSUN-Bedroom datasets. Specifically, we achieve 2.69 FID with 9 NFE and 6.86 FID with 5 NFE on CIFAR-10 dataset, outperforming the SOTA method significantly. We further apply $S^3$ to Stable-Diffusion model and get an acceleration ratio of 2$\times$, showing the feasibility of sampling in very few steps without retraining of the neural network.

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AAAI Conference 2024 Conference Paper

Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning

Jiayu Chen
Zelai Xu
Yunfei Li
Chao Yu
Jiaming Song
Huazhong Yang
Fei Fang
Yu Wang

Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effective way to accelerate learning, but an under-explored dimension for generating a curriculum is the difficulty-to-learn of the subgames –games induced by starting from a specific state. In this work, we present a novel subgame curriculum learning framework for zero-sum games. It adopts an adaptive initial state distribution by resetting agents to some previously visited states where they can quickly learn to improve performance. Building upon this framework, we derive a subgame selection metric that approximates the squared distance to NE values and further adopt a particle-based state sampler for subgame generation. Integrating these techniques leads to our new algorithm, Subgame Automatic Curriculum Learning (SACL), which is a realization of the subgame curriculum learning framework. SACL can be combined with any MARL algorithm such as MAPPO. Experiments in the particle-world environment and Google Research Football environment show SACL produces much stronger policies than baselines. In the challenging hide-and-seek quadrant environment, SACL produces all four emergent stages and uses only half the samples of MAPPO with self-play. The project website is at https://sites.google.com/view/sacl-neurips.

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NeurIPS Conference 2024 Conference Paper

Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study

Xuefei Ning
Zifu Wang
Shiyao Li
Zinan Lin
Peiran Yao
Tianyu Fu
Matthew B. Blaschko
Guohao Dai

Teaching to improve student models (e. g. , knowledge distillation) is an extensively studied methodology in LLMs. However, in human education, teaching enhances not only the students but also the teachers by fostering more rigorous and clearer reasoning, as well as deeper knowledge building. We ask: Can LLMs also learn by teaching (LbT) for better reasoning? If the answer is yes, we can potentially unlock the possibility of continuously advancing the models without solely relying on human-produced data or stronger models. In this paper, we provide a preliminary exploration of this question. We show that LbT ideas can be incorporated into existing LLM training/prompting pipelines and bring improvements. Specifically, we design three methods, each mimicking one of the three levels of LbT: observing students' feedback, learning from the feedback, and learning iteratively, with the goal of improving answer accuracy without training or improving models' inherent capability with fine-tuning. We reveal some findings: (1) Teaching materials that make it easier for students to learn (via in-context learning) have clearer and more accurate logic; (2) Weak-to-strong generalization: LbT might help improve strong models by teaching weak models; (3) Diversity in students might help: teaching multiple students could be better than teaching a single student or the teacher alone. We hope that our exploration can inspire future research on LbT and, more broadly, the adoption of advanced education techniques to improve LLMs. The code and website are at https: //github. com/imagination-research/lbt and https: //sites. google. com/view/llm-learning-by-teaching.

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ICML Conference 2024 Conference Paper

Evaluating Quantized Large Language Models

Shiyao Li
Xuefei Ning
Luning Wang
Tengxuan Liu
Xiangsheng Shi
Shengen Yan
Guohao Dai 0001
Huazhong Yang

Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, StableLM, Gemma, and Mamba, with parameters ranging from 125M to 180B. The evaluation encompasses five types of tasks: basic NLP, emergent ability, trustworthiness, dialogue, and long-context tasks. Moreover, we also evaluate the state-of-the-art (SOTA) quantization methods to demonstrate their applicability. Based on the extensive experiments, we systematically summarize the effect of quantization, provide recommendations to apply quantization techniques, and point out future directions. The code can be found in https: //github. com/thu-nics/qllm-eval.

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ICLR Conference 2024 Conference Paper

Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation

Xuefei Ning
Zinan Lin 0001
Zixuan Zhou
Zifu Wang
Huazhong Yang
Yu Wang 0002

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can also potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization for inference efficiency, and showcases the potential of eliciting high-quality answers by explicitly planning the answer structure in language.

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AAMAS Conference 2023 Conference Paper

Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration

Chao Yu
Xinyi Yang
Jiaxuan Gao
Jiayu Chen
Yunfei Li
Jijia Liu
Yunfei Xiang
Ruixin Huang

We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MARL) has recently become a trending paradigm for solving this challenge. However, existing MARL-based methods adopt action-making steps as the metric for exploration efficiency by assuming all the agents are acting in a fully synchronous manner: i. e. , every single agent produces an action simultaneously and every single action is executed instantaneously at each time step. Despite its mathematical simplicity, such a synchronous MARL formulation can be problematic for real-world robotic applications. It can be typical that different robots may take slightly different wall-clock times to accomplish an atomic action or even periodically get lost due to hardware issues. Simply waiting for every robot being ready for the next action can be particularly time-inefficient. Therefore, we propose an asynchronous MARL solution, Asynchronous Coordination Explorer (ACE), to tackle this real-world challenge. We first extend a classical MARL algorithm, multi-agent PPO (MAPPO), to the asynchronous setting and additionally apply action-delay randomization to enforce the learned policy to generalize better to varying action delays in the real world. Moreover, each navigation agent is represented as a team-size-invariant CNN-based policy, which greatly benefits real-robot deployment by handling possible robot lost and allows bandwidth-efficient intra-agent communication through low-dimensional CNN features. We first validate our approach in a grid-based scenario. Both simulation and real-robot results show that ACE reduces over 10% actual exploration time compared with classical approaches. We also apply our framework Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), A. Ricci, W. Yeoh, N. Agmon, B. An (eds.), May 29 – June 2, 2023, London, United Kingdom. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). All rights reserved. to a high-fidelity visual-based environment, Habitat, achieving 28% improvement in exploration efficiency.

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AAAI Conference 2023 Conference Paper

Ensemble-in-One: Ensemble Learning within Random Gated Networks for Enhanced Adversarial Robustness

Yi Cai
Xuefei Ning
Huazhong Yang
Yu Wang

Adversarial attacks have threatened modern deep learning systems by crafting adversarial examples with small perturbations to fool the convolutional neural networks (CNNs). To alleviate that, ensemble training methods are proposed to facilitate better adversarial robustness by diversifying the vulnerabilities among the sub-models, simultaneously maintaining comparable natural accuracy as standard training. Previous practices also demonstrate that enlarging the ensemble can improve the robustness. However, conventional ensemble methods are with poor scalability, owing to the rapidly increasing complexity when containing more sub-models in the ensemble. Moreover, it is usually infeasible to train or deploy an ensemble with substantial sub-models, owing to the tight hardware resource budget and latency requirement. In this work, we propose Ensemble-in-One (EIO), a simple but effective method to efficiently enlarge the ensemble with a random gated network (RGN). EIO augments a candidate model by replacing the parametrized layers with multi-path random gated blocks (RGBs) to construct an RGN. The scalability is significantly boosted because the number of paths exponentially increases with the RGN depth. Then by learning from the vulnerabilities of numerous other paths within the RGN, every path obtains better adversarial robustness. Our experiments demonstrate that EIO consistently outperforms previous ensemble training methods with smaller computational overheads, simultaneously achieving better accuracy-robustness trade-offs than adversarial training methods under black-box transfer attacks. Code is available at https://github.com/cai-y13/Ensemble-in-One.git

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AAMAS Conference 2023 Conference Paper

Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation

Xinyi Yang
Shiyu Huang
Yiwen Sun
Yuxiang Yang
Chao Yu
Wei-Wei Tu
Huazhong Yang
Yu Wang

This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. However, it is inefficient for MARL to directly explore the (nearly) optimal policy in the large search space, which is exacerbated as the agent number increases (e. g. , 10+ agents) or the environment is more complex (e. g. , 3𝐷 simulator). Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy. In this paper, we propose Multi-Agent Graph-Enhanced Commander-EXecutor (MAGE-X), a graph-based goal-conditioned hierarchical method for multi-agent navigation tasks. MAGE-X comprises a high-level Goal Commander and a low-level Action Executor. The Goal Commander predicts the probability distribution of the goals and leverages them to assign the most appropriate final target to each agent. The Action Executor utilizes graph neural networks (GNN) to construct a subgraph for each agent that only contains its crucial partners to improve cooperation. Additionally, the Goal Encoder in the Action Executor captures the relationship between the agent and the designated goal to encourage the agent to reach the final target. The results show that MAGE-X outperforms the state-of-the-art MARL baselines with a 100% success rate with only 3 million training steps in multi-agent particle environments (MPE) with 50 agents, and at least a 12% higher success rate and 2× higher data efficiency in a more complicated quadrotor 3𝐷 navigation task.

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AAAI Conference 2023 Conference Paper

Memory-Oriented Structural Pruning for Efficient Image Restoration

Xiangsheng Shi
Xuefei Ning
Lidong Guo
Tianchen Zhao
Enshu Liu
Yi Cai
Yuhan Dong
Huazhong Yang

Deep learning (DL) based methods have significantly pushed forward the state-of-the-art for image restoration (IR) task. Nevertheless, DL-based IR models are highly computation- and memory-intensive. The surging demands for processing higher-resolution images and multi-task paralleling in practical mobile usage further add to their computation and memory burdens. In this paper, we reveal the overlooked memory redundancy of the IR models and propose a Memory-Oriented Structural Pruning (MOSP) method. To properly compress the long-range skip connections (a major source of the memory burden), we introduce a compactor module onto each skip connection to decouple the pruning of the skip connections and the main branch. MOSP progressively prunes the original model layers and the compactors to cut down the peak memory while maintaining high IR quality. Experiments on real image denoising, image super-resolution and low-light image enhancement show that MOSP can yield models with higher memory efficiency while better preserving performance compared with baseline pruning methods.

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ICML Conference 2023 Conference Paper

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models

Enshu Liu
Xuefei Ning
Zinan Lin 0001
Huazhong Yang
Yu Wang 0002

Diffusion probabilistic models (DPMs) are a new class of generative models that have achieved state-of-the-art generation quality in various domains. Despite the promise, one major drawback of DPMs is the slow generation speed due to the large number of neural network evaluations required in the generation process. In this paper, we reveal an overlooked dimension—model schedule—for optimizing the trade-off between generation quality and speed. More specifically, we observe that small models, though having worse generation quality when used alone, could outperform large models in certain generation steps. Therefore, unlike the traditional way of using a single model, using different models in different generation steps in a carefully designed model schedule could potentially improve generation quality and speed simultaneously. We design OMS-DPM, a predictor-based search algorithm, to determine the optimal model schedule given an arbitrary generation time budget and a set of pre-trained models. We demonstrate that OMS-DPM can find model schedules that improve generation quality and speed than prior state-of-the-art methods across CIFAR-10, CelebA, ImageNet, and LSUN datasets. When applied to the public checkpoints of the Stable Diffusion model, we are able to accelerate the sampling by 2x while maintaining the generation quality.

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ICRA Conference 2022 Conference Paper

Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration

Yuanfan Xu
Jincheng Yu
Jiahao Tang
Jiantao Qiu
Jian Wang 0030
Yuan Shen 0001
Yu Wang 0002
Huazhong Yang

Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark en-titled Explore-Bench. The benchmark involves various explo-ration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep rein-forcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and 12 × speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Fur-thermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https://github.com/efc-robot/Explore-Bench.

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NeurIPS Conference 2022 Conference Paper

TA-GATES: An Encoding Scheme for Neural Network Architectures

Xuefei Ning
Zixuan Zhou
Junbo Zhao
Tianchen Zhao
Yiping Deng
Changcheng Tang
Shuang Liang
Huazhong Yang

Neural architecture search tries to shift the manual design of neural network (NN) architectures to algorithmic design. In these cases, the NN architecture itself can be viewed as data and needs to be modeled. A better modeling could help explore novel architectures automatically and open the black box of automated architecture design. To this end, this work proposes a new encoding scheme for neural architectures, the Training-Analogous Graph-based ArchiTecture Encoding Scheme (TA-GATES). TA-GATES encodes an NN architecture in a way that is analogous to its training. Extensive experiments demonstrate that the flexibility and discriminative power of TA-GATES lead to better modeling of NN architectures. We expect our methodology of explicitly modeling the NN training process to benefit broader automated deep learning systems. The code is available at https: //github. com/walkerning/aw_nas.

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NeurIPS Conference 2021 Conference Paper

Evaluating Efficient Performance Estimators of Neural Architectures

Xuefei Ning
Changcheng Tang
Wenshuo Li
Zixuan Zhou
Shuang Liang
Huazhong Yang
Yu Wang

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one supernet between all architectures. Recently, zero-shot estimators (ZSEs) that involve no training are proposed to further reduce the architecture evaluation cost. Despite the high efficiency of these estimators, the quality of such estimations has not been thoroughly studied. In this paper, we conduct an extensive and organized assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to study the behavior of OSEs and ZSEs, and reveal their biases and variances. After analyzing how and why the OSE estimations are unsatisfying, we explore how to mitigate the correlation gap of OSEs from three perspectives. Through our analysis, we give out suggestions for future application and development of efficient architecture performance estimators. Furthermore, the analysis framework proposed in our work could be utilized in future research to give a more comprehensive understanding of newly designed architecture performance estimators. The code is available at https: //github. com/walkerning/aw_nas.

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NeurIPS Conference 2021 Conference Paper

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

Jiayu Chen
Yuanxin Zhang
Yuanfan Xu
Huimin Ma
Huazhong Yang
Jiaming Song
Yu Wang
Yi Wu

We introduce an automatic curriculum algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learning problems. We motivate our curriculum learning paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current curriculum, and curriculum update to a new task distribution. Local optimization over the second term suggests that the curriculum should gradually expand the training tasks from easy to hard. Our VACL algorithm implements this variational paradigm with two practical components, task expansion and entity curriculum, which produces a series of training tasks over both the task configurations as well as the number of entities in the task. Experiment results show that VACL solves a collection of sparse-reward problems with a large number of agents. Particularly, using a single desktop machine, VACL achieves 98% coverage rate with 100 agents in the simple-spread benchmark and reproduces the ramp-use behavior originally shown in OpenAI’s hide-and-seek project.

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