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Hao Yin

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

Deep Reinforcement Learning for Scalable Offline Three-Dimensional Packing

Hao Yin
Hongjie He
Fan Chen

With the increasing number of items requiring handling simultaneously in complex logistics, offline three-dimensional packing methods need to plan larger numbers of items. Existing deep reinforcement learning (DRL)-based packing methods cannot plan for large numbers of items while keeping high-quality solutions due to limited exploration space and high computational complexity. To address this issue, this paper proposes a scalable DRL-based packing method. An attention-based pack-Q-network (PQNet) is constructed to learn the optimal packing policy by integrating unpacked items, available spaces, and packed items. To expand the valid exploration space, a bidding-based multi-policy (BBMP) framework composed of multiple PQNets is designed to efficiently explore more latent valid solutions, thus enhancing solution quality. To reduce computational complexity, a training-free dynamic candidate selection (DCS) framework is proposed to incorporate comprehensive item information during execution with minimal computation overhead, which helps in effectively planning large numbers of items. Experimental results show that across item numbers of 20~1000, our method consistently outperforms the best-performing baseline at each tested scale by 3.2%~13.1% in space utilization.

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AAAI Conference 2026 Conference Paper

PosPrune: Visual Token Pruning with Positional Bias Correction for Efficient Large Vision-Language Models

Ziyang Wang
Mengwei Li
Hao Yin
Wenhao Liu
Zilei Wang

Large Vision-Language Models (LVLMs) enhance performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LLMs). However, the large number of visual tokens introduces significant computational overhead. Existing token pruning methods either perform global selection via [CLS]-based attention in the vision encode or prune within LLM decoding layers. These approaches face two key challenges: (1) [CLS]-based attention primarily focuses on visually salient regions across the entire image, often overlooking semantically important tokens essential for reasoning; and (2) strong positional bias in the shallow decoder layers causes the model to favor later-positioned tokens, while neglecting earlier ones that may carry critical reasoning cues. To address these issues, we propose PosPrune, a training-free, two-stage visual token pruning framework. At the vision encoder, we introduce an Asymmetric Region-aware Pruning (ARP) strategy that retains more tokens in semantically rich regions while discarding more tokens from semantically less informative regions, thus preserving spatial diversity and task-relevant details. In the LLM decoding stage, we find that the positional bias in shallow layers is primarily driven by model architecture rather than task semantics. Based on this insight, we propose a novel Positional Bias Correction (PBC) mechanism to mitigate this bias. To further reduce redundancy, we apply Maximal Marginal Relevance (MMR) to select tokens that best balance textual relevance and diversity. Extensive experiments on various LVLMs and benchmarks demonstrate the general effectiveness of our approach. Notably, when applied to LLaVA-1.5-7B, PosPrune achieves a reduction of 85% in FLOPs while preserving 98.5% of the original performance.

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IJCAI Conference 2025 Conference Paper

FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation

Peng Wang
Shoupeng Lu
Hao Yin
Banglie Yang
Tianli Zhu
Cheng Dai

Federated learning (FL) enables collaborative training among multiple clients while preserving data privacy. However, its practical application is significantly limited by two major challenges: statistical heterogeneity and data distribution drift. Statistical heterogeneity causes the direction of local model updates to deviate from the global training objective, while data distribution drift leads to a mismatch between local models and their cluster models. To address these challenges, this paper proposes an adaptive clustered federated learning framework, Fed-CM. Initially, by capturing the dynamic patterns of personalized layer parameters in clients' models, Fed-CM effectively characterizes the correlations and distributional similarities among clients, reflecting the underlying statistical heterogeneity. Subsequently, this framework leverages client similarities to construct an undirected graph and adaptively performs effective cluster discovery with minimal dependence on hyperparameters. Furthermore, a monitoring strategy tracks the deviation between clients’ update directions and the dominant update direction of their clusters and then adaptively migrates clients experiencing data drift. Such a dynamic strategy helps maintain intra-cluster homogeneity and addresses the mismatch between local models and their cluster models. Compared to other state-of-the-art methods, experimental results on multiple datasets demonstrate that the proposed Fed-CM framework effectively addresses the challenges posed by statistical heterogeneity and data drift, significantly improving the performance and robustness of federated learning models.

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ICRA Conference 2025 Conference Paper

Learning Three-Dimensional Bin Packing with Adjustable-Order Semi-Online Setting

Hao Yin
Chenxi Zhang
Fan Chen 0003
Hongjie He 0005

The online setting brings greater flexibility and practicality to the three-dimensional bin packing problem (3DBPP) but at the cost of algorithm performance. Existing methods mitigate the performance impact by introducing semionline settings with look-ahead or buffer zones. However, these methods either fail to fundamentally alter the packing order or reduce packing efficiency. This paper proposes a novel semionline setting that allows for the observation of multiple items and the selection of one for packing, thereby adjusting the packing order without reducing packing efficiency. We do work for solving the semi-online packing problem via reinforcement learning which faces two real-world challenges: (1) a variable and difficult-to-predict number of observed items, and (2) the obstruction of robotic arm movement by already packed items. On the one hand, we design a policy network capable of adapting to variable item quantities. On the other hand, we introduce a guided bottom-up packing reward function to free up space for robotic arm motion. We show that our method outperforms the baselines in terms of space utilization with the condition of observing at least two items. Further experiments demonstrate the functionality of our reward function, which can guide a virtual robot to complete packing tasks.

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NeurIPS Conference 2025 Conference Paper

The Mirage of Performance Gains: Why Contrastive Decoding Fails to Mitigate Object Hallucinations in MLLMs?

Hao Yin
Guangzong Si
Zilei Wang

Contrastive decoding strategies are widely used to reduce object hallucinations in multimodal large language models (MLLMs). These methods work by constructing contrastive samples to induce hallucinations and then suppressing them in the output distribution. However, this paper demonstrates that such approaches fail to effectively mitigate the hallucination problem. The performance improvements observed on POPE Benchmark are largely driven by two misleading factors: (1) crude, unidirectional adjustments to the model’s output distribution and (2) the adaptive plausibility constraint, which reduces the sampling strategy to greedy search. To further illustrate these issues, we introduce a series of spurious improvement methods and evaluate their performance against contrastive decoding techniques. Experimental results reveal that the observed performance gains in contrastive decoding are entirely unrelated to its intended goal of mitigating hallucinations. Our findings challenge common assumptions about the effectiveness of contrastive decoding strategies and pave the way for developing genuinely effective solutions to hallucinations in MLLMs.

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IROS Conference 2025 Conference Paper

Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework

Deteng Zhang
Junjie Zhang
Yan Sun
Tao Li
Hao Yin
Hongzhao Xie
Jie Yin

Considerable advancements have been achieved in SLAM methods tailored for structured environments, yet their robustness under challenging corner cases remains a critical limitation. Although multi-sensor fusion approaches integrating diverse sensors have shown promising performance improvements, the research community faces two key barriers: On one hand, the lack of standardized and configurable benchmarks that systematically evaluate SLAM algorithms under diverse degradation scenarios hinders comprehensive performance assessment. While on the other hand, existing SLAM frameworks primarily focus on fusing a limited set of sensor types, without effectively addressing adaptive sensor selection strategies for varying environmental conditions. To bridge these gaps, we make three key contributions: First, we introduce M3DGR dataset: a sensor-rich benchmark with systematically induced degradation patterns including visual challenge, LiDAR degeneracy, wheel slippage and GNSS denial. Second, we conduct a comprehensive evaluation of forty SLAM systems on M3DGR, providing critical insights into their robustness and limitations under challenging real-world conditions. Third, we develop a resilient modular multi-sensor fusion framework named Ground-Fusion++, which demonstrates robust performance by coupling GNSS, RGB-D, LiDAR, IMU (Inertial Measurement Unit) and wheel odometry. Codes 1 and datasets 2 are publicly available.

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ICLR Conference 2024 Conference Paper

Policy Rehearsing: Training Generalizable Policies for Reinforcement Learning

Chengxing Jia
Chen-Xiao Gao
Hao Yin
Fuxiang Zhang
Xiong-Hui Chen
Tian Xu 0003
Lei Yuan 0005
Zongzhang Zhang

Human beings can make adaptive decisions in a preparatory manner, i.e., by making preparations in advance, which offers significant advantages in scenarios where both online and offline experiences are expensive and limited. Meanwhile, current reinforcement learning methods commonly rely on numerous environment interactions but hardly obtain generalizable policies. In this paper, we introduce the idea of \textit{rehearsal} into policy optimization, where the agent plans for all possible outcomes in mind and acts adaptively according to actual responses from the environment. To effectively rehearse, we propose ReDM, an algorithm that generates a diverse and eligible set of dynamics models and then rehearse the policy via adaptive training on the generated model set. Rehearsal enables the policy to make decision plans for various hypothetical dynamics and to naturally generalize to previously unseen environments. Our experimental results demonstrate that ReDM is capable of learning a valid policy solely through rehearsal, even with \emph{zero} interaction data. We further extend ReDM to scenarios where limited or mismatched interaction data is available, and our experimental results reveal that ReDM produces high-performing policies compared to other offline RL baselines.

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AAMAS Conference 2024 Conference Paper

Solving Offline 3D Bin Packing Problem with Large-sized Bin via Two-stage Deep Reinforcement Learning

Hao Yin
Fan Chen
Hongjie He

Existing Deep Reinforcement Learning (DRL) algorithms address the 3D Bin Packing Problem (3D-BPP) by decomposing the packing action into three sub-stages. However, this three-stage scheme makes it necessary for information to be passed between subnetworks, which may increase the computational cost of training and inference. This paper proposes a two-stage DRL algorithm, combining index and orientation into a single sub-stage to simplify learning. Additionally, a Bidirectional Cooperative Packing (BCP) method is introduced to compress the action space during position selection while retaining exploration capability. The experimental results show that the two-stage DRL algorithm, which incorporates BCP, achieves 0. 3%-1. 7% improvement in space utilization compared to the currently best-performing algorithm.

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AAAI Conference 2023 Conference Paper

Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers

Lei Yuan
Ziqian Zhang
Ke Xue
Hao Yin
Feng Chen
Cong Guan
Lihe Li
Chao Qian

Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be promising for many real-world applications. Previous works mainly focus on improving coordination ability via solving MARL-specific challenges (e.g., non-stationarity, credit assignment, scalability), but ignore the policy perturbation issue when testing in a different environment. This issue hasn't been considered in problem formulation or efficient algorithm design. To address this issue, we firstly model the problem as a Limited Policy Adversary Dec-POMDP (LPA-Dec-POMDP), where some coordinators from a team might accidentally and unpredictably encounter a limited number of malicious action attacks, but the regular coordinators still strive for the intended goal. Then, we propose Robust Multi-Agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers (ROMANCE), which enables the trained policy to encounter diversified and strong auxiliary adversarial attacks during training, thus achieving high robustness under various policy perturbations. Concretely, to avoid the ego-system overfitting to a specific attacker, we maintain a set of attackers, which is optimized to guarantee the attackers high attacking quality and behavior diversity. The goal of quality is to minimize the ego-system coordination effect, and a novel diversity regularizer based on sparse action is applied to diversify the behaviors among attackers. The ego-system is then paired with a population of attackers selected from the maintained attacker set, and alternately trained against the constantly evolving attackers. Extensive experiments on multiple scenarios from SMAC indicate our ROMANCE provides comparable or better robustness and generalization ability than other baselines.

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NeurIPS Conference 2022 Conference Paper

Efficient Multi-agent Communication via Self-supervised Information Aggregation

Cong Guan
Feng Chen
Lei Yuan
Chenghe Wang
Hao Yin
Zongzhang Zhang
Yang Yu

Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). To obtain meaningful information for decision-making, previous works typically combine raw messages generated by teammates with local information as inputs for policy. However, neglecting the aggregation of multiple messages poses great inefficiency for policy learning. Motivated by recent advances in representation learning, we argue that efficient message aggregation is essential for good coordination in MARL. In this paper, we propose Multi-Agent communication via Self-supervised Information Aggregation (MASIA), with which agents can aggregate the received messages into compact representations with high relevance to augment the local policy. Specifically, we design a permutation invariant message encoder to generate common information aggregated representation from raw messages and optimize it via reconstructing and shooting future information in a self-supervised manner. Each agent would utilize the most relevant parts of the aggregated representation for decision-making by a novel message extraction mechanism. Empirical results demonstrate that our method significantly outperforms strong baselines on multiple cooperative MARL tasks for various task settings.

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AAAI Conference 2021 Short Paper

Enhancing Context-Based Meta-Reinforcement Learning Algorithms via An Efficient Task Encoder (Student Abstract)

Feng Xu
Shengyi Jiang
Hao Yin
Zongzhang Zhang
Yang Yu
Ming Li
Dong Li
Wulong Liu

Meta-Reinforcement Learning (meta-RL) algorithms enable agents to adapt to new tasks from small amounts of exploration, based on the experience of similar tasks. Recent studies have pointed out that a good representation of a task is key to the success of off-policy context-based meta-RL. Inspired by contrastive methods in unsupervised representation learning, we propose a new method to learn the task representation based on the mutual information between transition tuples in a trajectory and the task embedding. We also propose a new estimation for task similarity based on Q-function, which can be used to form a constraint on the distribution of the encoded task variables, making the task encoder encode the task variables more effective on new tasks. Experiments on meta-RL tasks show that the newly proposed method outperforms existing meta-RL algorithms.

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ICML Conference 2017 Conference Paper

Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions

Yichen Chen
Dongdong Ge
Mengdi Wang 0001
Zizhuo Wang 0001
Yinyu Ye 0001
Hao Yin

Consider the regularized sparse minimization problem, which involves empirical sums of loss functions for $n$ data points (each of dimension $d$) and a nonconvex sparsity penalty. We prove that finding an $\mathcal{O}(n^{c_1}d^{c_2})$-optimal solution to the regularized sparse optimization problem is strongly NP-hard for any $c_1, c_2\in [0, 1)$ such that $c_1+c_2<1$. The result applies to a broad class of loss functions and sparse penalty functions. It suggests that one cannot even approximately solve the sparse optimization problem in polynomial time, unless P $=$ NP.

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TIST Journal 2017 Journal Article

UMCR

Hao Yin
Wei Wang
Xu Zhang
Yongqiang Lyu
Geyong Min
Dongchao Guo

Although mobile application ecosystems have experienced tremendous growth in recent years, retrieving content of mobile applications that serves a key to mobile content search engines still faces grand challenges. Compared to web content retrieval, it is much more difficult to capture content in mobile applications due to the diversity of applications and the lack of Uniform Resource Locator indices. In this study, we propose and implement a <underline>u</underline>ser interaction-driven <underline>m</underline>obile <underline>c</underline>ontent <underline>r</underline>etrieval (UMCR) system to address such issues, which is the first mobile content crawler in the current literature. UMCR is a distributed system that contains many measurement nodes, each of which combines the user interaction path traversing (UIPT) and Deep Package Inspection (DPI) together to obtain mobile content. UIPT determines the events of user interactions in various applications to capture the static content such as text and images, in which a traversal depth termination scheme and an optional cut-off component are adopted to balance the content coverage and traversing efficiency. Meanwhile, the analysis based on DPI is responsible for extracting the videos as well as digging the infrastructural information and performance metrics. In addition, a distributed traversal scheduling method is designed for UIPT tasks to improve the throughput and scalability in large-scale content retrieval. Experiments on retrieving content of 64 real mobile applications demonstrate that UMCR can handle diverse mobile applications efficiently. The scheduler can improve throughput by 3 times compared to the legacy arbitrary task assignment strategy.

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