GenAI4ELab/papercli-papers huggingface dataset:
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently
Authors: Yuanhe Zhang; Fanghui Liu; Yudong Chen
Venue: ICML 2025 (ICML 2025 oral)

Abstract: This paper explores how theory can guide and enhance practical algorithms, using Low-Rank Adaptation (LoRA) (Hu et al., 2022) in large language models as a case study. We rigorously prove that, under gradient descent, LoRA adapters align with specific singular subspaces of the one-step full fine-tuning gradient. This result suggests that, by properly initializing the adapters using the one-step full gradient, subspace alignment can be achieved immediately—applicable to both linear and nonlinear models. Building on our theory, we propose a theory-driven algorithm, LoRA-One, where the linear convergence (as well as generalization) is built and incorporating preconditioners theoretically helps mitigate the effects of ill-conditioning. Besides, our theory reveals connections between LoRA-One and other gradient-alignment-based methods, helping to clarify misconceptions in the design of such algorithms. LoRA-One achieves significant empirical improvements over LoRA and its variants across benchmarks in natural language understanding, mathematical reasoning, and code generation. Code is available at: https://github.com/YuanheZ/LoRA-One.
HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training
Authors: Geon-Woo Kim; Junbo Li; Shashidhar Gandham; Omar Baldonado; Adithya Gangidi; Pavan Balaji; Zhangyang Wang; Aditya Akella
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Training large language models (LLMs) increasingly relies on geographically distributed accelerators, causing prohibitive communication costs across regions and uneven utilization of heterogeneous hardware. We propose HALoS, a hierarchical asynchronous optimization framework that tackles these issues by introducing local parameter servers (LPSs) within each region and a global parameter server (GPS) that merges updates across regions. This hierarchical design minimizes expensive inter-region communication, reduces straggler effects, and leverages fast intra-region links. We provide a rigorous convergence analysis for HALoS under non-convex objectives, including theoretical guarantees on the role of hierarchical momentum in asynchronous training. Empirically, HALoS attains up to 7.5× faster convergence than synchronous baselines in geo-distributed LLM training and improves upon existing asynchronous methods by up to 2.1×. Crucially, HALoS preserves the model quality of fully synchronous SGD—matching or exceeding accuracy on standard language modeling and downstream benchmarks—while substantially lowering total training time. These results demonstrate that hierarchical, server-side update accumulation and global model merging are powerful tools for scalable, efficient training of new-era LLMs in heterogeneous, geo-distributed environments.
ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior
Authors: Zhongweiyang Xu; Xulin Fan; Zhong-Qiu Wang; Xilin Jiang; Romit Roy Choudhury
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Blind Speech Separation (BSS) aims to separate multiple speech sources from audio mixtures
recorded by a microphone array. The problem is
challenging because it is a blind inverse problem,
i.e., the microphone array geometry, the room impulse response (RIR), and the speech sources, are
all unknown. We propose ArrayDPS to solve the
BSS problem in an unsupervised, array-agnostic,
and generative manner. The core idea builds on
diffusion posterior sampling (DPS), but unlike
DPS where the likelihood is tractable, ArrayDPS
must approximate the likelihood by formulating
a separate optimization problem. The solution to the optimization approximates room acoustics
and the relative transfer functions between microphones. These approximations, along with the
diffusion priors, iterate through the ArrayDPS
sampling process and ultimately yield separated
voice sources. We only need a simple single-speaker speech diffusion model as a prior, along
with the mixtures recorded at the microphones; no
microphone array information is necessary. Evaluation results show that ArrayDPS outperforms
all baseline unsupervised methods while being
comparable to supervised methods in terms of
SDR. Audio demos and codes are provided at:
https://arraydps.github.io/ArrayDPSDemo/ and
https://github.com/ArrayDPS/ArrayDPS.
Permutation Equivariant Neural Networks for Symmetric Tensors
Authors: Edward Pearce-Crump
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Incorporating permutation equivariance into neural networks has proven to be useful in ensuring that models respect symmetries that exist in
data. Symmetric tensors, which naturally appear
in statistics, machine learning, and graph theory,
are essential for many applications in physics,
chemistry, and materials science, amongst others. However, existing research on permutation
equivariant models has not explored symmetric
tensors as inputs, and most prior work on learning
from these tensors has focused on equivariance
to Euclidean groups. In this paper, we present
two different characterisations of all linear permutation equivariant functions between symmetric
power spaces of $\mathbb{R}^{n}$. We show on two tasks that
these functions are highly data efficient compared
to standard MLPs and have potential to generalise
well to symmetric tensors of different sizes.
Flow Q-Learning
Authors: Seohong Park; Qiyang Li; Sergey Levine
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We present flow Q-learning (FQL), a simple and performant offline reinforcement learning (RL) method that leverages an expressive flow-matching policy to model arbitrarily complex action distributions in data. Training a flow policy with RL is a tricky problem, due to the iterative nature of the action generation process. We address this challenge by training an expressive one-step policy with RL, rather than directly guiding an iterative flow policy to maximize values. This way, we can completely avoid unstable recursive backpropagation, eliminate costly iterative action generation at test time, yet still mostly maintain expressivity. We experimentally show that FQL leads to strong performance across 73 challenging state- and pixel-based OGBench and D4RL tasks in offline RL and offline-to-online RL.
Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Authors: Jungtaek Kim
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of efficiently finding a global optimum of an expensive-to-evaluate black-box function. In general, a probabilistic regression model is widely used as a surrogate function to model an explicit distribution over function evaluations given an input to estimate and a training dataset. Beyond the probabilistic regression-based methods, density ratio estimation-based Bayesian optimization has been suggested in order to estimate a density ratio of the groups relatively close and relatively far to a global optimum. Developing this line of research further, supervised classifiers are employed to estimate a class probability for the two groups instead of a density ratio. However, the supervised classifiers used in this strategy are prone to be overconfident for known knowledge on global solution candidates. Supposing that we have access to unlabeled points, e.g., predefined fixed-size pools, we propose density ratio estimation-based Bayesian optimization with semi-supervised learning to solve this challenge. Finally, we show the empirical results of our methods and several baseline methods in two distinct scenarios with unlabeled point sampling and a fixed-size pool, and analyze the validity of our methods in diverse experiments.
Task-Gated Multi-Expert Collaboration Network for Degraded Multi-Modal Image Fusion
Authors: Yiming Sun; Xin Li; Pengfei Zhu; Qinghua Hu; Dongwei Ren; Huiying Xu; Xinzhong Zhu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multi-modal image fusion aims to integrate complementary information from different modalities to enhance perceptual capabilities in applications such as rescue and security. However, real-world imaging often suffers from degradation issues, such as noise, blur, and haze in visible imaging, as well as stripe noise in infrared imaging, which significantly degrades model performance. To address these challenges, we propose a task-gated multi-expert collaboration network (TG-ECNet) for degraded multi-modal image fusion. The core of our model lies in the task-aware gating and multi-expert collaborative framework, where the task-aware gating operates in two stages: degradation-aware gating dynamically allocates expert groups for restoration based on degradation types, and fusion-aware gating guides feature integration across modalities to balance information retention between fusion and restoration tasks. To achieve this, we design a two-stage training strategy that unifies the learning of restoration and fusion tasks. This strategy resolves the inherent conflict in information processing between the two tasks, enabling all-in-one multi-modal image restoration and fusion. Experimental results demonstrate that TG-ECNet significantly enhances fusion performance under diverse complex degradation conditions and improves robustness in downstream applications. The code is available at https://github.com/LeeX54946/TG-ECNet.
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking
Authors: Yuchun Miao; Sen Zhang; Liang Ding; Yuqi Zhang; Lefei Zhang; Dacheng Tao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: This work identifies the *Energy Loss Phenomenon* in Reinforcement Learning from Human Feedback (RLHF) and its connection to reward hacking. Specifically,  energy loss in the final layer of a Large Language Model (LLM) gradually increases during the RL process, with an *excessive* increase in energy loss characterizing reward hacking. Beyond empirical analysis, we further provide a theoretical foundation by proving that, under mild conditions, the increased energy loss reduces the upper bound of contextual relevance in LLMs, which is a critical aspect of reward hacking as the reduced contextual relevance typically indicates overfitting to reward model-favored patterns in RL. To address this issue, we propose an *Energy loss-aware PPO algorithm (EPPO)* which penalizes the increase in energy loss in the LLM's final layer during reward calculation to prevent excessive energy loss, thereby mitigating reward hacking. We theoretically show that EPPO can be conceptually interpreted as an entropy-regularized RL algorithm, which provides deeper insights into its effectiveness. Extensive experiments across various LLMs and tasks demonstrate the commonality of the energy loss phenomenon, as well as the effectiveness of EPPO in mitigating reward hacking and improving RLHF performance.
Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces
Authors: Henry Moss; Sebastian W. Ober; Tom Diethe
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Bayesian optimisation in the latent space of a VAE is a powerful framework for optimisation tasks over complex structured domains, such as the space of valid molecules. However, existing approaches tightly couple the surrogate and generative models, which can lead to suboptimal performance when the latent space is not tailored to specific tasks, which in turn has led to the proposal of increasingly sophisticated algorithms. In this work, we explore a new direction, instead proposing a decoupled approach that trains a generative model and a GP surrogate separately, then combines them via a simple yet principled Bayesian update rule. This separation allows each component to focus on its strengths— structure generation from the VAE and predictive modelling by the GP. We show that our decoupled approach improves our ability to identify high-potential candidates in molecular optimisation problems under constrained evaluation budgets.
Preserving AUC Fairness in Learning with Noisy Protected Groups
Authors: Mingyang Wu; Li Lin; Wenbin Zhang; Xin Wang; Zhenhuan Yang; Shu Hu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The Area Under the ROC Curve (AUC) is a key metric for classification, especially under class imbalance, with growing research focus on optimizing AUC over accuracy in applications like medical image analysis and deepfake detection. This leads to fairness in AUC optimization becoming crucial as biases can impact protected groups. While various fairness mitigation techniques exist, fairness considerations in AUC optimization remain in their early stages, with most research focusing on improving AUC fairness under the
assumption of clean protected groups. However, these studies often overlook the impact of noisy protected groups, leading to fairness violations in practice. To address this, we propose the first robust AUC fairness approach under noisy protected groups with fairness theoretical guarantees using distributionally robust optimization. Extensive experiments on tabular and image datasets show that our method outperforms state-of-the-art approaches in preserving AUC fairness. The code is in https://github.com/Purdue-M2/AUC_Fairness_with_Noisy_Groups.
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers
Authors: Xuanlei Zhao; Shenggan Cheng; Chang Chen; Zangwei Zheng; Ziming Liu; Zheming Yang; Yang You
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Scaling multi-dimensional transformers to long sequences is indispensable across various domains. However, the challenges of large memory requirements and slow speeds of such sequences necessitate sequence parallelism. All existing approaches fall under the category of embedded sequence parallelism, which are limited to shard along a single sequence dimension, thereby introducing significant communication overhead. However, the nature of multi-dimensional transformers involves independent calculations across multiple sequence dimensions. To this end, we propose Dynamic Sequence Parallelism (DSP) as a novel abstraction of sequence parallelism. DSP dynamically switches the parallel dimension among all sequences according to the computation stage with efficient resharding strategy. DSP offers significant reductions in communication costs, adaptability across modules, and ease of implementation with minimal constraints. Experimental evaluations demonstrate DSP's superiority over state-of-the-art embedded sequence parallelism methods by remarkable throughput improvements ranging from 32.2% to 10x, with less than 25% communication volume.
Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
Authors: Zhuocheng Gong; Jian Guan; Wei Wu; Huishuai Zhang; Dongyan Zhao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) have achieved remarkable success, yet aligning their generations with human preferences remains a critical challenge. Existing approaches to preference modeling often rely on an explicit or implicit reward function, overlooking the intricate and multifaceted nature of human preferences that may encompass conflicting factors across diverse tasks and populations. To address this limitation, we introduce Latent Preference Coding (LPC), a novel framework that models the implicit factors as well as their combinations behind holistic preferences using discrete latent codes. LPC seamlessly integrates with various offline alignment algorithms, automatically inferring the underlying factors and their importance from data without relying on pre-defined reward functions and hand-crafted combination weights. Extensive experiments on multiple benchmarks demonstrate that LPC consistently improves upon three alignment algorithms (DPO, SimPO, and IPO) using three base models (Mistral-7B, Llama3-8B, and Llama3-Instruct-8B). Furthermore, deeper analysis reveals that the learned latent codes effectively capture the differences in the distribution of human preferences and significantly enhance the robustness of alignment algorithms against noise in data. By providing a unified representation for the multifarious preference factors, LPC paves the way towards developing more robust and versatile alignment techniques for responsible deployment of powerful LLMs.
Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing
Authors: Ye Du; Chen Yang; Nanxi Yu; Wanyu Lin; Qian Zhao; Shujun Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: *De novo* peptide sequencing is a fundamental computational technique for ascertaining amino acid sequences of peptides directly from tandem mass spectrometry data, eliminating the need for reference databases. Cutting-edge models encode the observed mass spectra into latent representations from which peptides are predicted auto-regressively. However, the issue of missing fragmentation, attributable to factors such as suboptimal fragmentation efficiency and instrumental constraints, presents a formidable challenge in practical applications. To tackle this obstacle, we propose a novel computational paradigm called $\underline{\textbf{L}}$atent $\underline{\textbf{I}}$mputation before $\underline{\textbf{P}}$rediction (LIPNovo). LIPNovo is devised to compensate for missing fragmentation information within observed spectra before executing the final peptide prediction. Rather than generating raw missing data, LIPNovo performs imputation in the latent space, guided by the theoretical peak profile of the target peptide sequence. The imputation process is conceptualized as a set-prediction problem, utilizing a set of learnable peak queries to reason about the relationships among observed peaks and directly generate the latent representations of theoretical peaks through optimal bipartite matching. In this way, LIPNovo manages to supplement missing information during inference and thus boosts performance. Despite its simplicity, experiments on three benchmark datasets demonstrate that LIPNovo outperforms state-of-the-art methods by large margins. Code is available at https://github.com/usr922/LIPNovo.
Robust Secure Swap: Responsible Face Swap With Persons of Interest Redaction and Provenance Traceability
Authors: Yunshu Dai; Jianwei Fei; Fangjun Huang; Chip Hong Chang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: As AI generative models evolve, face swap technology has become increasingly accessible, raising concerns over potential misuse. Celebrities may be manipulated without consent, and ordinary individuals may fall victim to identity fraud. To address these threats, we propose Secure Swap, a method that protects persons of interest (POI) from face-swapping abuse and embeds a unique, invisible watermark into nonPOI swapped images for traceability. By introducing an ID Passport layer, Secure Swap redacts POI faces and generates watermarked outputs for nonPOI. A detachable watermark encoder and decoder are trained with the model to ensure provenance tracing. 
Experimental results demonstrate that Secure Swap not only preserves face swap functionality but also effectively prevents unauthorized swaps of POI and detects different embedded model's watermarks with high accuracy. 
Specifically, our method achieves a 100% success rate in protecting POI and over 99% watermark extraction accuracy for nonPOI. Besides fidelity and effectiveness, the robustness of protected models against image-level and model-level attacks in both online and offline application scenarios is also experimentally demonstrated.
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
Authors: Zijie Qiu; Jiaqi Wei; Xiang Zhang; Sheng Xu; Kai Zou; Zhi Jin; ZhiQiang Gao; Nanqing Dong; Siqi Sun
Venue: ICML 2025 (ICML 2025 poster)

Abstract: De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribution of noise signals, leading to data-specific biases. We present RankNovo, the first deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models. RankNovo employs a list-wise reranking approach, modeling candidate peptides as multiple sequence alignments and utilizing axial attention to extract informative features across candidates. Additionally, we introduce two new metrics, PMD (**P**eptide **M**ass **D**eviation) and RMD (**R**esidual**M**ass **D**eviation), which offer delicate supervision by quantifying mass differences between peptides at both the sequence and residue levels. Extensive experiments demonstrate that RankNovo not only surpasses its base models used to generate training candidates for reranking pre-training, but also sets a new state-of-the-art benchmark. Moreover, RankNovo exhibits strong zero-shot generalization to unseen models—those whose generations were not exposed during training, highlighting its robustness and potential as a universal reranking framework for peptide sequencing. Our work presents a novel reranking strategy that fundamentally challenges existing single-model paradigms and advances the frontier of accurate de novo sequencing. Our source code is provided on GitHub.
New Bounds for Sparse Variational Gaussian Processes
Authors: Michalis Titsias
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Sparse variational Gaussian processes (GPs) construct tractable posterior approximations to GP  models. At the core of these methods is the assumption that the true posterior distribution over training function values ${\bf f}$ and inducing variables ${\bf u}$ is approximated by a variational distribution that incorporates the conditional GP prior $p({\bf f} | {\bf u})$ in its factorization. While  this assumption is considered as fundamental, we show that for model training we can relax it through the use of a more general variational distribution $q({\bf f} | {\bf u} )$ that depends on $N$ extra parameters, where  $N$ is the number of training examples. In GP regression, we can analytically optimize the evidence lower bound over the extra parameters and express a tractable collapsed bound that is tighter than the previous bound. The new bound is also amenable to stochastic optimization and its implementation requires minor modifications to existing sparse GP code. Further, we also describe extensions to non-Gaussian likelihoods. On several datasets we demonstrate that our method can reduce  bias when learning the hyperparameters and can lead to better predictive performance.
SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding
Authors: Woohyeon Park; Woojin Kim; Jaeik Kim; Jaeyoung Do
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Despite significant advancements in Vision-Language Models (VLMs), the performance of existing VLMs remains hindered by object hallucination, a critical challenge to achieving accurate visual understanding. To address this issue, we propose SECOND: Selective and Contrastive Decoding, a novel approach that enables VLMs to effectively leverage multi-scale visual information with an object-centric manner, closely aligning with human visual perception. SECOND progressively selects and integrates multi-scale visual information, facilitating a more precise interpretation of images. By contrasting these visual information iteratively, SECOND significantly reduces perceptual hallucinations and outperforms a wide range of benchmarks. Our theoretical analysis and experiments highlight the largely unexplored potential of multi-scale application in VLMs, showing that prioritizing and contrasting across scales outperforms existing methods.