GenAI4ELab/papercli-papers huggingface dataset:
Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models
Authors: Yangxu Liao; Wenke Huang; Guancheng Wan; Jian Liang; Bin Yang; Mang Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Federated learning provides an efficient privacy-preserving distributed training framework for large language models, addressing the growing scarcity of publicly available training data while enabling the utilization of private datasets. While integrating large language model fine-tuning with federated learning emerges as a promising research direction, researchers pay limited attention to non-IID instruction-following scenarios. Our key insight is decomposing client updates into consensus and divergence components, enabling the model to maintain core capabilities while adapting to domain-specific knowledge. We propose a novel federated learning framework called **FedICU** (Splitting with **I**mportan**C**e-aware **U**pdating for Heterogeneous **Fed**erated Learning with Large Language Models), which introduces an aggregation mechanism that dynamically balances these components based on their contribution to global model performance, while implementing an importance-aware parameter updating strategy to prevent catastrophic forgetting and domain overfitting. Extensive experiments across diverse domains demonstrate that FedICU significantly outperforms existing federated learning approaches in terms of both generalization performance and domain adaptation. Our code is available at https://github.com/liaosunny123/FedICU.
Bayesian Active Learning for Bivariate Causal Discovery
Authors: Yuxuan Wang; Mingzhou Liu; Xinwei Sun; Wei Wang; Yizhou Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Determining the direction of relationships between variables is fundamental for understanding complex systems across scientific domains. While observational data can uncover relationships between variables, it cannot distinguish between cause and effect without experimental interventions. To effectively uncover causality, previous works have proposed intervention strategies that sequentially optimize the intervention values. However, most of these approaches primarily maximized information-theoretic gains that may not effectively measure the reliability of direction determination. In this paper, we formulate the causal direction identification as a hypothesis-testing problem, and propose a Bayes factor-based intervention strategy, which can quantify the evidence strength of one hypothesis (*e.g.*, causal) over the other (*e.g.*, non-causal). To balance the immediate and future gains of testing strength, we propose a sequential intervention objective over intervention values in multiple steps. By analyzing the objective function, we develop a dynamic programming algorithm that reduces the complexity from non-polynomial to polynomial. Experimental results on bivariate systems, tree-structured graphs, and an embodied AI environment demonstrate the effectiveness of our framework in direction determination and its extensibility to both multivariate settings and real-world applications.
Adapting Precomputed Features for Efficient Graph Condensation
Authors: Yuan Li; Jun Hu; Zemin Liu; Bryan Hooi; Jia Chen; Bingsheng He
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Graph Neural Networks (GNNs) face significant computational challenges when handling large-scale graphs. To address this, Graph Condensation (GC) methods aim to compress large graphs into smaller, synthetic ones that are more manageable for GNN training. Recently, trajectory matching methods have shown state-of-the-art (SOTA) performance for GC, aligning the model's training behavior on a condensed graph with that on the original graph by guiding the trajectory of model parameters. However, these approaches require repetitive GNN retraining during condensation, making them computationally expensive. To address the efficiency issue, we completely bypass trajectory matching and propose a novel two-stage framework. The first stage, a precomputation stage, performs one-time message passing to extract structural and semantic information from the original graph. The second stage, a diversity-aware adaptation stage, performs class-wise alignment while maximizing the diversity of synthetic features. Remarkably, even with just the precomputation stage, which takes only seconds, our method either matches or surpasses 5 out of 9 baseline results. Extensive experiments show that our approach achieves comparable or better performance while being 96× to 2,455× faster than SOTA methods, making it more practical for large-scale GNN applications. Our code and data are available at https://github.com/Xtra-Computing/GCPA.
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
Authors: Jie Hu; Yi-Ting Ma; Do Young Eun
Venue: ICML 2025 (ICML 2025 oral)

Abstract: We propose a *history-driven target (HDT)* framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\\boldsymbol{\\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies. However, SRRW's reliance on explicit computation of transition probabilities for all neighbors at each step introduces substantial computational overhead, while its strict dependence on time-reversible Markov chains excludes advanced non-reversible MCMC methods. To overcome these limitations, instead of direct modification of transition kernel, HDT introduces a history-dependent target distribution $\\boldsymbol{\\pi}[\\mathbf{x}]$ to replace the original target $\\boldsymbol{\\mu}$ in any graph sampler, where $\\mathbf{x}$ represents the empirical measure of past visits. This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\\boldsymbol{\\mu}$ and near-zero variance performance. Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used (LRU) cache ensures scalability to large general graphs.
Transfer Learning for Nonparametric Contextual Dynamic Pricing
Authors: Fan Wang; Feiyu Jiang; Zifeng Zhao; Yi Yu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Dynamic pricing strategies are crucial for firms to maximize revenue by adjusting prices based on market conditions and customer characteristics. However, designing optimal pricing strategies becomes challenging when historical data are limited, as is often the case when launching new products or entering new markets.  One promising approach to overcome this limitation is to leverage information from related products or markets to inform the focal pricing decisions. In this paper, we explore transfer learning for nonparametric contextual dynamic pricing under a covariate shift model, where the marginal distributions of covariates differ between source and target domains while the reward functions remain the same. We propose a novel Transfer Learning for Dynamic Pricing (TLDP) algorithm that can effectively leverage pre-collected data from a source domain to enhance pricing decisions in the target domain. The regret upper bound of TLDP is established under a simple Lipschitz condition on the reward function.  To establish the optimality of TLDP, we further derive a matching minimax lower bound, which includes the target-only scenario as a special case and is  presented for the first time in the literature. Extensive numerical experiments validate our approach, demonstrating its superiority over existing methods and highlighting its practical utility in real-world applications.
Learning to Quantize for Training Vector-Quantized Networks
Authors: Peijia Qin; Jianguo Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Deep neural networks incorporating discrete latent variables have shown significant potential in sequence modeling.
A notable approach is to leverage vector quantization (VQ) to generate discrete representations within a codebook.
However, its discrete nature prevents the use of standard backpropagation, which has led to challenges in efficient codebook training.
In this work, we introduce **Meta-Quantization (MQ)**, a novel vector quantization training framework inspired by meta-learning.
Our method separates the optimization of the codebook and the auto-encoder into two levels.
Furthermore, we introduce a hyper-net to replace the embedding-parameterized codebook, enabling the codebook to be dynamically generated based on the feedback from the auto-encoder.
Different from previous VQ objectives, our innovation results in a meta-objective that makes the codebook training task-aware.
We validate the effectiveness of MQ with VQVAE and VQGAN architecture on image reconstruction and generation tasks.
Experimental results showcase the superior generative performance of MQ, underscoring its potential as a robust alternative to existing VQ methods.
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety
Authors: Wonjun Lee; Doehyeon Lee; Eugene Choi; Sangyoon Yu; Ashkan Yousefpour; Haon Park; Bumsub Ham; Suhyun Kim
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated evaluation methods, but these methods struggle to detect implicit harmful content or produce inaccurate evaluations. Therefore, we found that existing benchmarks have low levels of harmfulness, ambiguous data, and limited diversity in image-text pair combinations. To address these issues, we propose the ELITE benchmark, a high-quality safety evaluation benchmark for VLMs, underpinned by our enhanced evaluation method, the ELITE evaluator. The ELITE evaluator explicitly incorporates a toxicity score to accurately assess harmfulness in multimodal contexts, where VLMs often provide specific, convincing, but unharmful descriptions of images. We filter out ambiguous and low-quality image-text pairs from existing benchmarks using the ELITE evaluator and generate diverse combinations of safe and unsafe image-text pairs. Our experiments demonstrate that the ELITE evaluator achieves superior alignment with human evaluations compared to prior automated methods, and the ELITE benchmark offers enhanced benchmark quality and diversity. By introducing ELITE, we pave the way for safer, more robust VLMs, contributing essential tools for evaluating and mitigating safety risks in real-world applications.
Discriminative Policy Optimization for Token-Level Reward Models
Authors: Hongzhan Chen; Tao Yang; Shiping Gao; Ruijun Chen; Xiaojun Quan; Hongtao Tian; Ting Yao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Process reward models (PRMs) provide more nuanced supervision compared to outcome reward models (ORMs) for optimizing policy models, positioning them as a promising approach to enhancing the capabilities of LLMs in complex reasoning tasks. Recent efforts have advanced PRMs from step-level to token-level granularity by integrating reward modeling into the training of generative models, with reward scores derived from token generation probabilities. However, the conflict between generative language modeling and reward modeling may introduce instability and lead to inaccurate credit assignments. To address this challenge, we revisit token-level reward assignment by decoupling reward modeling from language generation and derive a token-level reward model through the optimization of a discriminative policy, termed the Q-function Reward Model (Q-RM). We theoretically demonstrate that Q-RM explicitly learns token-level Q-functions from preference data without relying on fine-grained annotations. In our experiments, Q-RM consistently outperforms all baseline methods across various benchmarks. 
For example, when integrated into PPO/REINFORCE algorithms, Q-RM enhances the average Pass@1 score by 5.85/4.70 points on mathematical reasoning tasks compared to the ORM baseline, and by 4.56/5.73 points compared to the token-level PRM counterpart. Moreover, reinforcement learning with Q-RM significantly enhances training efficiency, achieving convergence 12× faster than ORM on GSM8K and 11× faster than step-level PRM on MATH. Code and data are available at https://github.com/homzer/Q-RM.
Avoiding Leakage Poisoning: Concept Interventions Under Distribution Shifts
Authors: Mateo Espinosa Zarlenga; Gabriele Dominici; Pietro Barbiero; Zohreh Shams; Mateja Jamnik
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In this paper, we investigate how concept-based models (CMs) respond to out-of-distribution (OOD) inputs. CMs are interpretable neural architectures that first predict a set of high-level *concepts* (e.g., "stripes", "black") and then predict a task label from those concepts. In particular, we study the impact of *concept interventions* (i.e., operations where a human expert corrects a CM’s mispredicted concepts at test time) on CMs' task predictions when inputs are OOD. Our analysis reveals a weakness in current state-of-the-art CMs, which we term *leakage poisoning*, that prevents them from properly improving their accuracy when intervened on for OOD inputs. To address this, we introduce *MixCEM*, a new CM that learns to dynamically exploit leaked information missing from its concepts only when this information is in-distribution. Our results across tasks with and without complete sets of concept annotations demonstrate that MixCEMs outperform strong baselines by significantly improving their accuracy for both in-distribution and OOD samples in the presence and absence of concept interventions.
Latent Mamba Operator for Partial Differential Equations
Authors: Karn Tiwari; Niladri Dutta; N M Anoop Krishnan; Prathosh AP
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Neural operators have emerged as powerful data-driven frameworks for solving Partial Differential Equations (PDEs), offering significant speedups over numerical methods. However, existing neural operators struggle with scalability in high-dimensional spaces, incur high computational costs, and face challenges in capturing continuous and long-range dependencies in PDE dynamics. To address these limitations, we introduce the Latent Mamba Operator (LaMO), which integrates the efficiency of state-space models (SSMs) in latent space with the expressive power of kernel integral formulations in neural operators. We also establish a theoretical connection between state-space models (SSMs) and the kernel integral of neural operators. Extensive experiments across diverse PDE benchmarks on regular grids, structured meshes, and point clouds covering solid and fluid physics datasets, LaMOs achieve consistent state-of-the-art (SOTA) performance, with a 32.3\% improvement over existing baselines in solution operator approximation, highlighting its efficacy in modeling complex PDEs solution.
PlaySlot: Learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning
Authors: Angel Villar-Corrales; Sven Behnke
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Predicting future scene representations is a crucial task for enabling robots to understand and
interact with the environment. However, most existing methods rely on videos and simulations
with precise action annotations, limiting their ability to leverage the large amount of avail-
able unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric
video prediction model that infers object representations and latent actions from unlabeled
video sequences. It then uses these representations to forecast future object states and video
frames. PlaySlot allows the generation of multiple possible futures conditioned on latent actions,
which can be inferred from video dynamics, provided by a user, or generated by a learned action
policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot
outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video
demonstrations. Videos and code are available on our project website.
WildChat-50M: A Deep Dive Into the Role of Synthetic Data in Post-Training
Authors: Benjamin Feuer; Chinmay Hegde
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Language model (LLM) post-training can refine behaviors and unlock new skills, but the open science supporting these post-training techniques is still in its infancy. One limiting factor has been the difficulty of conducting large-scale comparative analyses of synthetic data generating models and LLM judges. To close this gap, we introduce WildChat-50M, the largest public chat dataset to date. We extend the existing WildChat dataset to include responses not only from GPT, but from over 50 different open-weight models, ranging in size from 0.5B to 104B parameters. We conduct an extensive comparative analysis and demonstrate the potential of this dataset by creating Re-Wild, our own public SFT mix, which outperforms the recent Tulu-3 SFT mixture from Allen AI with only 40% as many samples.
How to Train Your Multi-Exit Model? Analyzing the Impact of Training Strategies
Authors: Piotr Kubaty; Bartosz Wójcik; Bartłomiej Tomasz Krzepkowski; Monika Michaluk; Tomasz Trzcinski; Jary Pomponi; Kamil Adamczewski
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Early exits enable the network's forward pass to terminate early by attaching trainable internal classifiers to the backbone network. Existing early-exit methods typically adopt either a joint training approach, where the backbone and exit heads are trained simultaneously, or a disjoint approach, where the heads are trained separately. However, the implications of this choice are often overlooked, with studies typically adopting one approach without adequate justification. This choice influences training dynamics and its impact remains largely unexplored. In this paper, we introduce a set of metrics to analyze early-exit training dynamics and guide the choice of training strategy. We demonstrate that conventionally used joint and disjoint regimes yield suboptimal performance. To address these limitations, we propose a mixed training strategy: the backbone is trained first, followed by the training of the entire multi-exit network. Through comprehensive evaluations of training strategies across various architectures, datasets, and early-exit methods we present strengths and weaknesses of the early exit training strategies. In particular, we show consistent improvements in performance and efficiency using the proposed mixed strategy.
am-ELO: A Stable Framework for Arena-based LLM Evaluation
Authors: Zirui Liu; Jiatong Li; Yan Zhuang; Qi Liu; Shuanghong Shen; Jie Ouyang; Mingyue Cheng; Shijin Wang
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models (LLMs). Existing framework based on ELO rating system suffers from the inevitable instability problem due to ranking inconsistency and the lack of attention to the varying abilities of annotators. In this paper, we introduce a novel stable arena framework to address these issues by enhancing the ELO Rating System. Specifically, we replace the iterative update method with a Maximum Likelihood Estimation (MLE) approach, m-ELO, and provide theoretical proof of the consistency and stability of the MLE approach for model ranking. Additionally, we proposed the am-ELO, which modify the Elo Rating’s probability function to incorporate annotator abilities, enabling the simultaneous estimation of model scores and annotator reliability. Experiments demonstrate that this method ensures stability, proving that this framework offers a more robust, accurate, and stable evaluation method for LLMs.
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models
Authors: Daniil Laptev; Nikita Balagansky; Yaroslav Aksenov; Daniil Gavrilov
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of feature evolution, enabling fine-grained interpretability and mechanistic insights into model computations. Crucially, we demonstrate how these cross-layer feature maps facilitate direct steering of model behavior by amplifying or suppressing chosen features, achieving targeted thematic control in text generation. Together, our findings highlight the utility of a causal, cross-layer interpretability framework that not only clarifies how features develop through forward passes but also provides new means for transparent manipulation of large language models.
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient
Authors: Jan Ludziejewski; Maciej Pióro; Jakub Krajewski; Maciej Stefaniak; Michał Krutul; Jan Małaśnicki; Marek Cygan; Piotr Sankowski; Kamil Adamczewski; Piotr Miłoś; Sebastian Jaszczur
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mixture of Experts (MoE) architectures have significantly increased computational efficiency in both research and real-world applications of large-scale machine learning models. However, their scalability and efficiency under memory constraints remain relatively underexplored. In this work, we present joint scaling laws for dense and MoE models, incorporating key factors such as the number of active parameters, dataset size, and the number of experts. Our findings provide a principled framework for selecting the optimal MoE configuration under fixed memory and compute budgets. Surprisingly, we show that MoE models can be more memory-efficient than dense models, contradicting conventional wisdom. Extensive empirical validation confirms the theoretical predictions of our scaling laws. These results offer actionable insights for designing and deploying MoE models in practical large-scale training scenarios.
LLM Data Selection and Utilization via Dynamic Bi-level Optimization
Authors: Yang Yu; Kai Han; Hang Zhou; Yehui Tang; Kaiqi Huang; Yunhe Wang; Dacheng Tao
Venue: ICML 2025 (ICML 2025 poster)

Abstract: While large-scale training data is fundamental for developing capable large language models (LLMs), strategically selecting high-quality data has emerged as a critical approach to enhance training efficiency and reduce computational costs. Current data selection methodologies predominantly rely on static, training-agnostic criteria, failing to account for the dynamic model training and data interactions. In this paper, we propose a new Data Weighting Model (DWM) to adjust the weight of selected data within each batch to achieve a dynamic data utilization during LLM training. Specially, to better capture the dynamic data preference of the trained model, a bi-level optimization framework is implemented to update the weighting model. Our experiments demonstrate that DWM enhances the performance of models trained with randomly-selected data, and the learned weighting model can be transferred to enhance other data selection methods and models of different sizes. Moreover, we further analyze how a model’s data preferences evolve throughout training, providing new insights into the data preference of the model during training.