GenAI4ELab/papercli-papers huggingface dataset:
Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation
Authors: Satoki Ishikawa; Rio Yokota; Ryo Karakida
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Local learning, which trains a network through layer-wise local targets and losses, has been studied as an alternative to backpropagation (BP) in neural computation. However, its algorithms often become more complex or require additional hyperparameters due to the locality, making it challenging to identify desirable settings where the algorithm progresses in a stable manner.
To provide theoretical and quantitative insights, we introduce  maximal update parameterization ($\mu$P) in the infinite-width limit for two representative designs of local targets: predictive coding (PC) and target propagation (TP). We verify that $\mu$P enables hyperparameter transfer across models of different widths.
Furthermore, our analysis reveals unique and intriguing properties of $\mu$P that are not present in conventional BP. By analyzing deep linear networks, we find that PC's gradients interpolate between first-order and Gauss-Newton-like gradients, depending on the parameterization.  
We demonstrate that, in specific standard settings, PC in the infinite-width limit behaves more similarly to the first-order gradient.
For TP, even with the standard scaling of the last layer differing from classical $\mu$P, its local loss optimization favors the feature learning regime over the kernel regime.
Towards Faster Decentralized Stochastic Optimization with Communication Compression
Authors: Rustem Islamov; Yuan Gao; Sebastian U Stich
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Communication efficiency has garnered significant attention as it is considered the main bottleneck for large-scale decentralized Machine Learning applications in distributed and federated settings. In this regime, clients are restricted to transmitting small amounts of compressed information to their neighbors over a communication graph. Numerous endeavors have been made to address this challenging problem by developing algorithms with compressed communication for decentralized non-convex optimization problems. Despite considerable efforts, current theoretical understandings of the problem are still very limited, and existing algorithms all suffer from various limitations. In particular, these algorithms typically rely on strong, and often infeasible assumptions such as bounded data heterogeneity or require large batch access while failing to achieve linear speedup with the number of clients. In this paper, we introduce MoTEF, a novel approach that integrates communication compression with $\textbf{Mo}$mentum $\textbf{T}$racking and $\textbf{E}$rror $\textbf{F}$eedback. MoTEF is the first algorithm to achieve an asymptotic rate matching that of distributed SGD under arbitrary data heterogeneity, hence resolving a long-standing theoretical obstacle in decentralized optimization with compressed communication. We provide numerical experiments to validate our theoretical findings and confirm the practical superiority of MoTEF.
Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions
Authors: Rui Qiao; Zhaoxuan Wu; Jingtan Wang; Pang Wei Koh; Bryan Kian Hsiang Low
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Machine learning models often have uneven performance among subpopulations
(a.k.a., groups) in the data distributions. This poses a significant challenge for the
models to generalize when the proportions of the groups shift during deployment.
To improve robustness to such shifts, existing approaches have developed strategies
that train models or perform hyperparameter tuning using the group-labeled data
to minimize the worst-case loss over groups. However, a non-trivial amount of
high-quality labels is often required to obtain noticeable improvements. Given
the costliness of the labels, we propose to adopt a different paradigm to enhance
group label efficiency: utilizing the group-labeled data as a target set to optimize
the weights of other group-unlabeled data. We introduce Group-robust Sample
Reweighting (GSR), a two-stage approach that first learns the representations from
group-unlabeled data, and then tinkers the model by iteratively retraining its last
layer on the reweighted data using influence functions. Our GSR is theoretically
sound, practically lightweight, and effective in improving the robustness to sub-
population shifts. In particular, GSR outperforms the previous state-of-the-art
approaches that require the same amount or even more group labels. Our code is
available at https://github.com/qiaoruiyt/GSR.
Endless Jailbreaks with Bijection Learning
Authors: Brian R.Y. Huang; Maximilian Li; Leonard Tang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Despite extensive safety measures, LLMs are vulnerable to adversarial inputs, or jailbreaks, which can elicit unsafe behaviors. In this work, we introduce bijection learning, a powerful attack algorithm which automatically fuzzes LLMs for safety vulnerabilities using randomly-generated encodings whose complexity can be tightly controlled. We leverage in-context learning to teach models bijective encodings, pass encoded queries to the model to bypass built-in safety mechanisms, and finally decode responses back into English. Our attack is extremely effective on a wide range of frontier language models. By controlling complexity parameters such as number of key-value mappings in the encodings, we find a close relationship between the capability level of the attacked LLM and the average complexity of the most effective bijection attacks. Our work highlights that new vulnerabilities in frontier models can emerge with scale: more capable models are more severely jailbroken by bijection attacks.
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Authors: Sarp Aykent; Tian Xia
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships and symmetries inherent in such systems, especially in large-scale, dynamic molecular datasets. These methods often must balance trade-offs between expressiveness and computational efficiency, limiting their scalability. To address this gap, we propose a novel Geometric Tensor Network (GotenNet) that effectively models the geometric intricacies of 3D graphs while ensuring strict equivariance under the Euclidean group E(3). Our approach directly tackles the expressiveness-efficiency trade-off by leveraging effective geometric tensor representations without relying on irreducible representations or Clebsch-Gordan transforms, thereby reducing computational overhead. We introduce a unified structural embedding, incorporating geometry-aware tensor attention and hierarchical tensor refinement that iteratively updates edge representations through inner product operations on high-degree steerable features, allowing for flexible and efficient representations for various tasks. We evaluated models on QM9, rMD17, MD22, and Molecule3D datasets, where the proposed model consistently outperforms state-of-the-art methods in both scalar and high-degree property predictions, demonstrating exceptional robustness across diverse datasets, and establishes GotenNet as a versatile and scalable framework for 3D equivariant Graph Neural Networks.
Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition
Authors: Aliyah R. Hsu; Georgia Zhou; Yeshwanth Cherapanamjeri; Yaxuan Huang; Anobel Odisho; Peter R. Carroll; Bin Yu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Automated mechanistic interpretation research has attracted great interest due to its potential to scale explanations of neural network internals to large models. Existing automated circuit discovery work relies on activation patching or its approximations to identify subgraphs in models for specific tasks (circuits). They often suffer from slow runtime, approximation errors, and specific requirements of metrics, such as non-zero gradients.
In this work, we introduce contextual decomposition for transformers (CD-T) to build interpretable circuits in large language models. CD-T can produce circuits at any level of abstraction and is the first to efficiently produce circuits as fine-grained as attention heads at specific sequence positions.
CD-T is compatible to all transformer types, and requires no training or manually-crafted examples.
CD-T consists of a set of mathematical equations to isolate contribution of model features. Through recursively computing contribution of all nodes in a computational graph of a model using CD-T followed by pruning, we are able to reduce circuit discovery runtime from hours to seconds compared to state-of-the-art baselines.
On three standard circuit evaluation datasets (indirect object identification, greater-than comparisons, and docstring completion),
we demonstrate that CD-T outperforms ACDC and EAP by better recovering the manual circuits with an average of 97% ROC AUC under low runtimes.
In addition, we provide evidence that faithfulness of CD-T circuits is not due to random chance by showing our circuits are 80% more faithful than random circuits of up to 60% of the original model size. 
Finally, we show CD-T circuits are able to perfectly replicate original models' behavior(faithfulness  = 1) using fewer nodes than the baselines for all tasks.
Our results underscore the great promise of CD-T for efficient automated mechanistic interpretability, paving the way for new insights into the workings of large language models.
How to Evaluate Reward Models for RLHF
Authors: Evan Frick; Tianle Li; Connor Chen; Wei-Lin Chiang; Anastasios Nikolas Angelopoulos; Jiantao Jiao; Banghua Zhu; Joseph E. Gonzalez; Ion Stoica
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback).
The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance.
However, this process is prohibitively expensive.
To address this, we build a predictive model of downstream LLM performance by evaluating the reward model on proxy tasks. 
These proxy tasks consist of a large-scale human preference and a verifiable correctness preference dataset, in which we measure 12 metrics across 12 domains.
To investigate which reward model metrics are most correlated to gold-standard RLHF outcomes, we launch an end-to-end RLHF experiment on a large-scale crowd-sourced human preference platform to view real reward model downstream performance as ground truth. 
Ultimately, we compile our data and findings into Preference Proxy Evaluations (PPE), the first reward model benchmark explicitly linked to post-RLHF real-world human preference performance, which we opensource for public use and further development at https://github.com/lmarena/PPE.
An Efficient Framework for Crediting Data Contributors of Diffusion Models
Authors: MingYu Lu; Chris Lin; Chanwoo Kim; Su-In Lee
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: As diffusion models are deployed in real-world settings and their performance driven by training data, appraising the contribution of data contributors is crucial to creating incentives for sharing quality data and to implementing policies for data compensation. Depending on the use case, model performance corresponds to various global properties of the distribution learned by a diffusion model (e.g., overall aesthetic quality). Hence, here we address the problem of attributing global properties of diffusion models to data contributors. The Shapley value provides a principled approach to valuation by uniquely satisfying game-theoretic axioms of fairness. However, estimating Shapley values for diffusion models is computationally impractical because it requires retraining and rerunning inference on many subsets of data contributors. We introduce a method to efficiently retrain and rerun inference for Shapley value estimation, by leveraging model pruning and fine-tuning. We evaluate the utility of our method with three use cases: (i) image quality for a DDPM trained on a CIFAR dataset, (ii) demographic diversity for an LDM trained on CelebA-HQ, and (iii) aesthetic quality for a Stable Diffusion model LoRA-finetuned on Post-Impressionist artworks. Our results empirically demonstrate that our framework can identify important data contributors across global properties, outperforming existing attribution methods for diffusion models.
Decentralized Optimization with Coupled Constraints
Authors: Demyan Yarmoshik; Alexander Rogozin; Nikita Kiselev; Daniil Dorin; Alexander Gasnikov; Dmitry Kovalev
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We consider the decentralized minimization of a separable objective $\sum_{i=1}^{n} f_i(x_i)$, where the variables are coupled through an affine constraint $\sum_{i=1}^n\left(\mathbf{A}_i x_i - b_i\right) = 0$.
We assume that the functions $f_i$, matrices $\mathbf{A}_i$, and vectors $b_i$ are stored locally by the nodes of a computational network, and that the functions $f_i$ are smooth and strongly convex. 

This problem has significant applications in resource allocation and systems control and can also arise in distributed machine learning.
We propose lower complexity bounds for decentralized optimization problems with coupled constraints and a first-order algorithm achieving the lower bounds. To the best of our knowledge, our method is also the first linearly convergent first-order decentralized algorithm for problems with general affine coupled constraints.
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
Authors: Ayan Sengupta; Siddhant Chaudhary; Tanmoy Chakraborty
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model pruning techniques attempt to alleviate these issues by relying heavily on external calibration datasets to determine which parameters to prune or compress, thus limiting their flexibility and scalability across different compression ratios. Moreover, these methods often cause severe performance degradation, particularly in downstream tasks, when subjected to higher compression rates. In this paper, we propose *PruneNet*, a novel model compression method that addresses these limitations by reformulating model pruning as a policy learning process. PruneNet decouples the pruning process from the model architecture, eliminating the need for calibration datasets. It learns a stochastic pruning policy to assess parameter importance solely based on intrinsic model properties while preserving the spectral structure to minimize information loss. PruneNet can compress the LLaMA-2-7B model in just 15 minutes, achieving over 80\% retention of its zero-shot performance with a 30\% compression ratio, outperforming existing methods that retain only 75\% performance. Furthermore, on complex multitask language understanding tasks, PruneNet demonstrates its robustness by preserving up to 80\% performance of the original model, proving itself a superior alternative to conventional structured compression techniques.
FreDF: Learning to Forecast in the Frequency Domain
Authors: Hao Wang; Lichen Pan; Yuan Shen; Zhichao Chen; Degui Yang; Yifei Yang; Sen Zhang; Xinggao Liu; Haoxuan Li; Dacheng Tao
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Time series modeling presents unique challenges due to autocorrelation in both historical data and future sequences. While current research predominantly addresses autocorrelation within historical data, the correlations among future labels are often overlooked. Specifically, modern forecasting models primarily adhere to the Direct Forecast (DF) paradigm, generating multi-step forecasts independently and disregarding label correlations over time. In this work, we demonstrate that the learning objective of DF is biased in the presence of label correlation. To address this issue, we propose the Frequency-enhanced Direct Forecast (FreDF), which mitigates label correlation by learning to forecast in the frequency domain, thereby reducing estimation bias. Our experiments show that FreDF significantly outperforms existing state-of-the-art methods and is compatible with a variety of forecast models. Code is available at https://github.com/Master-PLC/FreDF.
SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration
Authors: Jintao Zhang; Jia wei; Pengle Zhang; Jun Zhu; Jianfei Chen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The transformer architecture predominates across various models. As the heart of the transformer, attention has a computational complexity of $O(N^2)$, compared to $O(N)$ for linear transformations. When handling large sequence lengths, attention becomes the primary time-consuming component. Although quantization has proven to be an effective method for accelerating model inference, existing quantization methods primarily focus on optimizing the linear layer.
In response, we first analyze the feasibility of quantization in attention detailedly. Following that, we propose SageAttention, a highly efficient and accurate quantization method for attention. The OPS (operations per second) of our approach outperforms FlashAttention2 and xformers by about 2.1x and 2.7x, respectively. SageAttention also achieves superior accuracy performance over FlashAttention3. Comprehensive experiments confirm that our approach incurs almost no end-to-end metrics loss across diverse models—including those for large language processing, image generation, and video generation. The code is available at https://github.com/thu-ml/SageAttention.
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
Authors: Jiyeon Kim; Hyunji Lee; Hyowon Cho; Joel Jang; Hyeonbin Hwang; Seungpil Won; Youbin Ahn; Dohaeng Lee; Minjoon Seo
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: In this work, we investigate how a model's tendency to broadly integrate its parametric knowledge evolves throughout pretraining, and how this behavior affects overall performance, particularly in terms of knowledge acquisition and forgetting. We introduce the concept of knowledge entropy, which quantifies the range of memory sources the model engages with; high knowledge entropy indicates that the model utilizes a wide range of memory sources, while low knowledge entropy suggests reliance on specific sources with greater certainty. Our analysis reveals a consistent decline in knowledge entropy as pretraining advances. We also find that the decline is closely associated with a reduction in the model's ability to acquire and retain knowledge, leading us to conclude that diminishing knowledge entropy (smaller number of active memory sources) impairs the model's knowledge acquisition and retention capabilities. We find further support for this by demonstrating that increasing the activity of inactive memory sources enhances the model's capacity for knowledge acquisition and retention.
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Authors: Yinan Zheng; Ruiming Liang; Kexin ZHENG; Jinliang Zheng; Liyuan Mao; Jianxiong Li; Weihao Gu; Rui Ai; Shengbo Eben Li; Xianyuan Zhan; Jingjing Liu
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary learning-based planning approaches such as imitation learning methods often struggle to balance competing objectives and lack of safety assurance,due to limited adaptability and inadequacy in learning complex multi-modal behaviors commonly exhibited in human planning, not to mention their strong reliance on the fallback strategy with predefined rules. We propose a novel transformer-based Diffusion Planner for closed-loop planning, which can effectively model multi-modal driving behavior and ensure trajectory quality without any rule-based refinement. Our model supports joint modeling of both prediction and planning tasks under the same architecture, enabling cooperative behaviors between vehicles. Moreover, by learning the gradient of the trajectory score function and employing a flexible classifier guidance mechanism, Diffusion Planner effectively achieves safe and adaptable planning behaviors. Evaluations on the large-scale real-world autonomous planning benchmark nuPlan and our newly collected 200-hour delivery-vehicle driving dataset demonstrate that Diffusion Planner achieves state-of-the-art closed-loop performance with robust transferability in diverse driving styles.
Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion
Authors: Jinbiao Chen; Jiahai Wang; Zhiguang Cao; Yaoxin Wu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Existing neural multi-objective combinatorial optimization (MOCO) methods still exhibit an optimality gap since they fail to fully exploit the intrinsic features of problem instances. A significant factor contributing to this shortfall is their reliance solely on graph-modal information. To overcome this, we propose a novel graph-image multimodal fusion (GIMF) framework that enhances neural MOCO methods by integrating graph and image information of the problem instances. Our GIMF framework comprises three key components: (1) a constructed coordinate image to better represent the spatial structure of the problem instance, (2) a problem-size adaptive resolution strategy during the image construction process to improve the cross-size generalization of the model, and (3) a multimodal fusion mechanism with modality-specific bottlenecks to efficiently couple graph and image information. We demonstrate the versatility of our GIMF by implementing it with two state-of-the-art neural MOCO backbones. Experimental results on classic MOCO problems show that our GIMF significantly outperforms state-of-the-art neural MOCO methods and exhibits superior generalization capability.
Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs
Authors: Shuo Li; Tao Ji; Xiaoran Fan; Linsheng Lu; Leyi Yang; Yuming Yang; Zhiheng Xi; Rui Zheng; Yuran Wang; xh.zhao; Tao Gui; Qi Zhang; Xuanjing Huang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In the study of LLMs, sycophancy represents a prevalent hallucination that poses significant challenges to these models. Specifically, LLMs often fail to adhere to original correct responses, instead blindly agreeing with users' opinions, even when those opinions are incorrect or malicious. However, research on sycophancy in visual language models (VLMs) has been scarce. In this work, we extend the exploration of sycophancy from LLMs to VLMs, introducing the MM-SY benchmark to evaluate this phenomenon. We present evaluation results from multiple representative models, addressing the gap in sycophancy research for VLMs. To mitigate sycophancy, we propose a synthetic dataset for training and employ methods based on prompts, supervised fine-tuning, and DPO. Our experiments demonstrate that these methods effectively alleviate sycophancy in VLMs. Additionally, we probe VLMs to assess the semantic impact of sycophancy and analyze the attention distribution of visual tokens. Our findings indicate that the ability to prevent sycophancy is predominantly observed in higher layers of the model. The lack of attention to image knowledge in these higher layers may contribute to sycophancy, and enhancing image attention at high layers proves beneficial in mitigating this issue.