GenAI4ELab/papercli-papers huggingface dataset:
ADBM: Adversarial Diffusion Bridge Model for Reliable Adversarial Purification
Authors: Xiao Li; Wenxuan Sun; Huanran Chen; Qiongxiu Li; Yingzhe He; Jie Shi; Xiaolin Hu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recently Diffusion-based Purification (DiffPure) has been recognized as an effective defense method against adversarial examples. However, we find DiffPure which directly employs the original pre-trained diffusion models for adversarial purification, to be suboptimal. This is due to an inherent trade-off between noise purification performance and data recovery quality. Additionally, the reliability of existing evaluations for DiffPure is questionable, as they rely on weak adaptive attacks. In this work, we propose a novel Adversarial Diffusion Bridge Model, termed ADBM. ADBM directly constructs a reverse bridge from the diffused adversarial data back to its original clean examples, enhancing the purification capabilities of the original diffusion models. Through theoretical analysis and experimental validation across various scenarios, ADBM has proven to be a superior and robust defense mechanism, offering significant promise for practical applications. Code is available at https://github.com/LixiaoTHU/ADBM.
A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation
Authors: Huimin Zeng; Xiaojie Wang; Anoop Jain; Zhicheng Dou; Dong Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Contrastive Learning (CL) proves to be effective for learning generalizable user representations in Sequential Recommendation (SR), but it suffers from high computational costs due to its reliance on negative samples. To overcome this limitation, we propose the first Non-Contrastive Learning (NCL) framework for SR, which eliminates computational overhead of identifying and generating negative
samples. However, without negative samples, it is challenging to learn uniform representations from only positive samples, which is prone to representation collapse. Furthermore, the alignment of the learned representations may be substantially compromised because existing ad-hoc augmentations can produce positive samples that have inconsistent user preferences. To tackle these challenges, we design a novel preference-preserving profile generation method to produce high-quality positive samples for non-contrastive training. Inspired by differential privacy, our approach creates augmented user profiles that exhibit high diversity while provably retaining consistent user preferences. With larger diversity and consistency of the positive samples, our NCL framework significantly enhances the alignment and uniformity of the learned representations, which contributes to better generalization. The experimental results on various benchmark datasets and model architectures demonstrate the effectiveness of the proposed method. Finally, our investigations reveal that both uniformity and alignment play a vital role in improving generalization for SR. Interestingly, in our data-sparse setting, alignment is usually more important than uniformity.
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis
Authors: Weronika Ormaniec; Felix Dangel; Sidak Pal Singh
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: The Transformer architecture has inarguably revolutionized deep learning, overtaking classical architectures like multi-layer perceptions (MLPs) and convolutional neural networks (CNNs). At its core, the attention block differs in form and functionality from most other architectural components in deep learning—to the extent that, in comparison to MLPs/CNNs, Transformers are more often accompanied by adaptive optimizers, layer normalization, learning rate warmup, etc. The root causes behind these outward manifestations and the precise mechanisms that govern them remain poorly understood. In this work, we bridge this gap by providing a fundamental understanding of what distinguishes the Transformer from the other architectures—grounded in a theoretical comparison of the (loss) Hessian. Concretely, for a single self-attention layer, (a) we first entirely derive the Transformer’s Hessian and express it in matrix derivatives; (b) we then characterize it in terms of data, weight, and attention moment dependencies; and (c) while doing so further highlight the important structural differences to the Hessian of classical networks. Our results suggest that various common architectural and optimization choices in Transformers can be traced back to their highly non-linear dependencies on the data and weight matrices, which vary heterogeneously across parameters. Ultimately, our findings provide a deeper understanding of the Transformer’s unique optimization landscape and the challenges it poses.
MoDeGPT: Modular Decomposition for Large Language Model Compression
Authors: Chi-Heng Lin; Shangqian Gao; James Seale Smith; Abhishek Patel; Shikhar Tuli; Yilin Shen; Hongxia Jin; Yen-Chang Hsu
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Large Language Models (LLMs) have significantly advanced AI with their exceptional performance across a wide range of tasks. However, their extensive computational requirements restrict their use on devices with limited resources.
While recent compression methods based on low-rank matrices show potential
solutions, they often suffer from significant loss of accuracy or introduce substantial
overhead in parameters and inference time. In this paper, we introduce Modular De-
composition (MoDeGPT), a new, efficient, and structured compression framework
that overcomes these limitations. MoDeGPT jointly decomposes pairs of consecu-
tive subcomponents within Transformer blocks, reduces hidden dimensions through
output reconstruction on a larger structural scale than conventional low-rank meth-
ods, and repurposes three classical matrix decomposition algorithms—Nyström
approximation, CR decomposition, and SVD—to ensure bounded errors in our
novel decomposition approach. Our experiments show that MoDeGPT, without
relying on backward propagation, consistently matches or surpasses the performance of prior techniques that depend on gradient information, while achieving a
98% reduction in compute costs when compressing a 13B-parameter model. On
LLaMA-2/3 and OPT models, MoDeGPT retains 90-95% of zero-shot performance
with compression rates of 25-30%. The compression process can be completed on
a single GPU in a few hours, boosting inference throughput by up to 46%.
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Authors: Zachary Ankner; Cody Blakeney; Kartik Sreenivasan; Max Marion; Matthew L Leavitt; Mansheej Paul
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In this work, we investigate whether small language models can determine high-quality subsets of large-scale text datasets that improve the performance of larger language models. While existing work has shown that pruning based on the perplexity of a larger model can yield high-quality data, we investigate whether smaller models can be used for perplexity-based pruning and how pruning is affected by the domain composition of the data being pruned. We demonstrate that for multiple dataset compositions, perplexity-based pruning of pretraining data can significantly improve downstream task performance: pruning based on perplexities computed with a 125 million parameter model improves the average performance on downstream tasks of a 3 billion parameter model by up to 2.04 and achieves up to a 1.45× reduction in pretraining steps to reach commensurate baseline performance. Furthermore, we demonstrate that such perplexity-based data pruning also yields downstream performance gains in the over-trained and data-constrained regimes.
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
Authors: Rui Hu; Yifan Zhang; Zhuoran Li; Longbo Huang
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found applications in various important tasks, attracting great research interest in their training algorithms. In general, GFlowNets are trained by fitting the forward flow to the backward flow on sampled training objects. Prior work focused on the choice of training objects, parameterizations, sampling and resampling strategies, and backward policies, aiming to enhance credit assignment, exploration, or exploitation of the training process. However, the choice of regression loss, which can highly influence the exploration and exploitation behavior of the under-training policy, has been overlooked. Due to the lack of theoretical understanding for choosing an appropriate regression loss, most existing algorithms train the flow network by minimizing the squared error of the forward and backward flows in log-space, i.e., using the quadratic regression loss. In this work, we rigorously prove that distinct regression losses correspond to specific divergence measures, enabling us to design and analyze regression losses according to the desired properties of the corresponding divergence measures. Specifically, we examine two key properties: zero-forcing and zero-avoiding, where the former promotes exploitation and higher rewards, and the latter encourages exploration and enhances diversity. Based on our theoretical framework, we propose three novel regression losses, namely, Shifted-Cosh, Linex(1/2), and Linex(1). We evaluate them across three benchmarks: hyper-grid, bit-sequence generation, and molecule generation. Our proposed losses are compatible with most existing training algorithms, and significantly improve the performances of the algorithms concerning convergence speed, sample diversity, and robustness.
Discriminator-Guided Embodied Planning for LLM Agent
Authors: Haofu Qian; Chenjia Bai; Jiatao Zhang; Fei Wu; Wei Song; Xuelong Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large Language Models (LLMs) have showcased remarkable reasoning capabilities in various domains, yet face challenges in complex embodied tasks due to the need for a coherent long-term policy and context-sensitive environmental understanding. Previous work performed LLM refinement relying on outcome-supervised feedback, which can be costly and ineffective. In this work, we introduce a novel framework, Discriminator-Guided Action Optimization (DGAP), for facilitating the optimization of LLM action plans via step-wise signals. Specifically, we employ a limited set of demonstrations to enable the discriminator to learn a score function, which assesses the alignment between LLM-generated actions and the underlying optimal ones at every step. Based on the discriminator, LLMs are prompted to generate actions that maximize the score, utilizing historical action-score pair trajectories as guidance. Under mild conditions, DGAP resembles critic-regularized optimization and has been demonstrated to achieve a stronger policy than the LLM planner. In experiments across different LLMs (GPT-4, Llama3-70B) in ScienceWorld and VirtualHome, our method achieves superior performance and better efficiency than previous methods.
Adam Exploits $\ell_\infty$-geometry of Loss Landscape via Coordinate-wise Adaptivity
Authors: Shuo Xie; Mohamad Amin Mohamadi; Zhiyuan Li
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Adam outperforms SGD when training language models. Yet this advantage is not well-understood theoretically --  previous convergence analysis for Adam and SGD mainly focuses on the number of steps $T$ and is already minimax-optimal in non-convex cases, which are both $\widetilde{O}(T^{-1/4})$. In this work, we argue that the exploitation of nice $\ell_\infty$-geometry is the key advantage of Adam over SGD. More specifically, we give a new convergence analysis for Adam under novel assumptions that loss is smooth under $\ell_\infty$-geometry rather than the more common $\ell_2$-geometry, which yields a much better empirical smoothness constant for GPT-2 and ResNet models. Our experiments confirm that Adam performs much worse when the favorable $\ell_\infty$-geometry is changed while SGD provably remains unaffected. We also extend the convergence analysis to blockwise Adam under novel blockwise smoothness assumptions.
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Authors: Haotian Zhang; Mingfei Gao; Zhe Gan; Philipp Dufter; Nina Wenzel; Forrest Huang; Dhruti Shah; Xianzhi Du; Bowen Zhang; Yanghao Li; Sam Dodge; Keen You; Zhen Yang; Aleksei Timofeev; Mingze Xu; Hong-You Chen; Jean-Philippe Fauconnier; Zhengfeng Lai; Haoxuan You; Zirui Wang; Afshin Dehghan; Peter Grasch; Yinfei Yang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning. Building upon the MM1 architecture, MM1.5 adopts a data-centric approach to model training, systematically exploring the impact of diverse data mixtures across the entire model training lifecycle. This includes high-quality OCR data and synthetic captions for continual pre-training, as well as an optimized visual instruction-tuning data mixture for supervised fine-tuning. Our models range from 1B to 30B parameters, encompassing both dense and mixture-of-experts (MoE) variants, and demonstrate that careful data curation and training strategies can yield strong performance even at small scales (1B and 3B). Additionally, we introduce two specialized variants: MM1.5-Video, designed for video understanding, and MM1.5-UI, tailored for mobile UI understanding. Through extensive empirical studies and ablations, we provide detailed insights into the training processes and decisions that inform our final designs, offering valuable guidance for future research in MLLM development.
Flow matching achieves almost minimax optimal convergence
Authors: Kenji Fukumizu; Taiji Suzuki; Noboru Isobe; Kazusato Oko; Masanori Koyama
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Flow matching (FM) has gained significant attention as a simulation-free generative model. Unlike diffusion models, which are based on stochastic differential equations, FM employs a simpler approach by solving an ordinary differential equation with an initial condition from a normal distribution, thus streamlining the sample generation process. This paper discusses the convergence properties of FM in terms of the $p$-Wasserstein distance, a measure of distributional discrepancy. We establish that FM can achieve an almost minimax optimal convergence rate for $1 \leq p \leq 2$, presenting the first theoretical evidence that FM can reach convergence rates comparable to those of diffusion models. Our analysis extends existing frameworks by examining a broader class of mean and variance functions for the vector fields and identifies specific conditions necessary to attain these optimal rates.
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
Authors: Yingwen Wu; Ruiji Yu; Xinwen Cheng; Zhengbao He; Xiaolin Huang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (DNNs). To achieve better detection performance, one type of approach proposes to fine-tune the model with auxiliary OOD datasets to amplify the difference between ID and OOD data through a separation loss defined on model outputs. However, none of these studies consider enlarging the feature disparity, which should be more effective compared to outputs. The main difficulty lies in the diversity of OOD samples, which makes it hard to describe their feature distribution, let alone design losses to separate them from ID features. In this paper, we neatly fence off the problem based on an aggregation property of ID features named Neural Collapse (NC). NC means that the penultimate features of ID samples within a class are nearly identical to the last layer weight of the corresponding class. Based on this property, we propose a simple but effective loss called Separation Loss, which binds the features of OOD data in a subspace orthogonal to the principal subspace of ID features formed by NC. In this way, the features of ID and OOD samples are separated by different dimensions. By optimizing the feature separation loss rather than purely enlarging output differences, our detection achieves SOTA performance on CIFAR10, CIFAR100 and ImageNet benchmarks without any additional data augmentation or sampling, demonstrating the importance of feature separation in OOD detection. Code is available
at https://github.com/Wuyingwen/Pursuing-Feature-Separation-for-OOD-Detection.
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Authors: Chejian Xu; Jiawei Zhang; Zhaorun Chen; Chulin Xie; Mintong Kang; Yujin Potter; Zhun Wang; Zhuowen Yuan; Alexander Xiong; Zidi Xiong; Chenhui Zhang; Lingzhi Yuan; Yi Zeng; Peiyang Xu; Chengquan Guo; Andy Zhou; Jeffrey Ziwei Tan; Xuandong Zhao; Francesco Pinto; Zhen Xiang; Yu Gai; Zinan Lin; Dan Hendrycks; Bo Li; Dawn Song
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have revealed vulnerabilities in these models, such as generating unsafe content by text-to-image models. Existing benchmarks on multimodal models either predominantly assess the helpfulness of these models, or only focus on limited perspectives such as fairness and privacy. In this paper, we present the first unified platform, MMDT (Multimodal DecodingTrust), designed to provide a comprehensive safety and trustworthiness evaluation for MMFMs. Our platform assesses models from multiple perspectives, including safety, hallucination, fairness/bias, privacy, adversarial robustness, and out-of-distribution (OOD) generalization. We have designed various evaluation scenarios and red teaming algorithms under different tasks for each perspective to generate challenging data, forming a high-quality benchmark. We evaluate a range of multimodal models using MMDT, and our findings reveal a series of vulnerabilities and areas for improvement across these perspectives. This work introduces the first comprehensive and unique safety and trustworthiness evaluation platform for MMFMs, paving the way for developing safer and more reliable MMFMs and systems. Our platform and benchmark are available at https://mmdecodingtrust.github.io/.
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms
Authors: Zhangheng LI; Keen You; Haotian Zhang; Di Feng; Harsh Agrawal; Xiujun Li; Mohana Prasad Sathya Moorthy; Jeffrey Nichols; Yinfei Yang; Zhe Gan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI understanding across a wide range of platforms, including iPhone, Android, iPad, Webpage, and AppleTV. Building on the foundation of Ferret-UI, Ferret-UI 2 introduces three key innovations: support for multiple platform types, high-resolution perception through adaptive scaling, and advanced task training data generation powered by GPT-4o with set-of-mark visual prompting. These advancements enable Ferret-UI 2 to perform complex, user-centered interactions, making it highly versatile and adaptable for the expanding diversity of platform ecosystems. Extensive empirical experiments on referring, grounding, user-centric advanced tasks (comprising 9 subtasks $\times$ 5 platforms), GUIDE next-action prediction dataset, and GUI-World multi-platform benchmark demonstrate that Ferret-UI 2 significantly outperforms Ferret-UI, and also shows strong cross-platform transfer capabilities.
MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models
Authors: Wenbo Hu; Jia-Chen Gu; Zi-Yi Dou; Mohsen Fayyaz; Pan Lu; Kai-Wei Chang; Nanyun Peng
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Existing multimodal retrieval benchmarks primarily focus on evaluating whether models can retrieve and utilize external textual knowledge for question answering. However, there are scenarios where retrieving visual information is either more beneficial or easier to access than textual data. 
In this paper, we introduce a multimodal retrieval-augmented generation benchmark, MRAG-Bench, in which we systematically identify and categorize scenarios where visually augmented knowledge is better than textual knowledge, for instance, more images from varying viewpoints.
MRAG-Bench consists of 16,130 images and 1,353 human-annotated multiple-choice questions across 9 distinct scenarios. With MRAG-Bench, we conduct an evaluation of 10 open-source and 4 proprietary large vision-language models (LVLMs). Our results show that all LVLMs exhibit greater improvements when augmented with images compared to textual knowledge, confirming that MRAG-Bench is vision-centric. Additionally, we conduct extensive analysis with MRAG-Bench, which offers valuable insights into retrieval-augmented LVLMs. Notably, the top-performing model, GPT-4o, faces challenges in effectively leveraging retrieved knowledge, achieving only a 5.82\% improvement with ground-truth information, in contrast to a 33.16\% improvement observed in human participants. These findings highlight the importance of MRAG-Bench in encouraging the community to enhance LVLMs' ability to utilize retrieved visual knowledge more effectively.
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning
Authors: Dohyeong Kim; Mineui Hong; Jeongho Park; Songhwai Oh
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In real-world applications, a reinforcement learning (RL) agent should consider multiple objectives and adhere to safety guidelines.
To address these considerations, we propose a constrained multi-objective RL algorithm named constrained multi-objective gradient aggregator (CoMOGA).
In the field of multi-objective optimization, managing conflicts between the gradients of the multiple objectives is crucial to prevent policies from converging to local optima.
It is also essential to efficiently handle safety constraints for stable training and constraint satisfaction.
We address these challenges straightforwardly by treating the maximization of multiple objectives as a constrained optimization problem (COP), where the constraints are defined to improve the original objectives.
Existing safety constraints are then integrated into the COP, and the policy is updated by solving the COP, which ensures the avoidance of gradient conflicts.
Despite its simplicity, CoMOGA guarantees convergence to global optima in a tabular setting.
Through various experiments, we have confirmed that preventing gradient conflicts is critical, and the proposed method achieves constraint satisfaction across all tasks.