GenAI4ELab/papercli-papers huggingface dataset:
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
Authors: Tue Minh Cao; Nhat Hoang-Xuan; Hieu Pham; Phi Le Nguyen; My T. Thai
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Understanding the inner workings of neural networks is essential for enhancing model performance and interpretability. Current research predominantly focuses on examining the connection between individual neurons and the model's final predictions, which suffers from challenges in interpreting the internal workings of the model, particularly when neurons encode multiple unrelated features. In this paper, we propose a novel framework that transitions the focus from analyzing individual neurons to investigating groups of neurons, shifting the emphasis from neuron-output relationships to the functional interactions between neurons. Our automated framework, NeurFlow, first identifies core neurons and clusters them into groups based on shared functional relationships, enabling a more coherent and interpretable view of the network’s internal processes. This approach facilitates the construction of a hierarchical circuit representing neuron interactions across layers, thus improving interpretability while reducing computational costs. Our extensive empirical studies validate the fidelity of our proposed NeurFlow. Additionally, we showcase its utility in practical applications such as image debugging and automatic concept labeling, thereby highlighting its potential to advance the field of neural network explainability.
Beyond FVD: An Enhanced Evaluation Metrics for Video Generation Distribution Quality
Authors: Ge Ya Luo; Gian Mario Favero; ZhiHao Luo; Alexia Jolicoeur-Martineau; Christopher Pal
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The Fréchet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, 
based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel.  Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average.

Project page: https://oooolga.github.io/JEDi.github.io/.
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
Authors: Renrui Zhang; Xinyu Wei; Dongzhi Jiang; Ziyu Guo; Yichi Zhang; Chengzhuo Tong; Jiaming Liu; Aojun Zhou; Shanghang Zhang; Peng Gao; Hongsheng Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Multi-modal Large Language Models (MLLMs) have recently showcased superior proficiency in general visual scenarios. However, we identify their mathematical capabilities remain under-explored with three areas to be improved: visual encoding of math diagrams, diagram-language alignment, and chain-of-thought (CoT) reasoning. This draws forth an urgent demand for an effective training paradigm and a large-scale, comprehensive dataset with detailed CoT rationales, which is challenging to collect and costly to annotate manually. To tackle this issue, we propose MAVIS, a MAthematical VISual instruction tuning pipeline for MLLMs, featuring an automatic data engine to efficiently create mathematical visual datasets.
We design the data generation process to be entirely independent of human intervention or GPT API usage, while ensuring the diagram-caption correspondence, question-answer correctness, and CoT reasoning quality. With this approach, we curate two datasets, MAVIS-Caption (558K diagram-caption pairs) and MAVIS-Instruct (834K visual math problems with CoT rationales), and propose four progressive stages for training MLLMs from scratch.
First, we utilize MAVIS-Caption to fine-tune a math-specific vision encoder (CLIP-Math) through contrastive learning, tailored for improved diagram visual encoding. Second, we also leverage MAVIS-Caption to align the CLIP-Math with a large language model (LLM) by a projection layer, enhancing vision-language alignment in mathematical domains. Third, we adopt MAVIS-Instruct to perform the instruction tuning for robust problem-solving skills, and term the resulting model as MAVIS-7B. Fourth, we apply Direct Preference Optimization (DPO) to enhance the CoT capabilities of our model, further refining its step-wise reasoning performance.
On various mathematical benchmarks, our MAVIS-7B achieves leading results among open-source MLLMs, e.g., surpassing other 7B models by +9.3% and the second-best LLaVA-NeXT (110B) by +6.9%, demonstrating the effectiveness of our method.
Graph Neural Networks Can (Often) Count Substructures
Authors: Paolo Pellizzoni; Till Hendrik Schulz; Karsten Borgwardt
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Message passing graph neural networks (GNNs) are known to have limited expressive power in their ability to distinguish some non-isomorphic graphs.
Because of this, it is well known that they are unable to detect or count arbitrary graph substructures (i.e., solving the subgraph isomorphism problem), a task that is of great importance for several types of graph-structured data. 
However, we observe that GNNs are in fact able to count graph patterns quite accurately across several real-world graph datasets.
Motivated by this observation, we provide an analysis of the subgraph-counting capabilities of GNNs beyond the worst case, deriving several sufficient conditions for GNNs to be able to count subgraphs and, more importantly, to be able to sample-efficiently learn to count subgraphs. 
Moreover, we develop novel dynamic programming algorithms for solving the subgraph isomorphism problem on restricted classes of pattern and target graphs, and show that message-passing GNNs can efficiently simulate these dynamic programs. 
Finally, we empirically validate that our sufficient conditions for GNNs to count subgraphs hold on many real-world datasets, providing a theoretically-grounded explanation to our motivating observations.
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Authors: Xiang Li; Cristina Mata; Jongwoo Park; Kumara Kahatapitiya; Yoo Sung Jang; Jinghuan Shang; Kanchana Ranasinghe; Ryan D Burgert; Mu Cai; Yong Jae Lee; Michael S Ryoo
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for robotic control remains challenging, particularly when constrained by a limited number of robot demonstrations. In this work, we introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations and enables an efficient transfer of a pretrained VLM into a powerful VLA, motivated by the success of visual instruction tuning in Computer Vision. First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets, aligning robotic actions with image pixel coordinates. Further, we enhance this dataset in a self-supervised manner by defining six auxiliary tasks, without requiring any additional action annotations. We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control. Through experiments across multiple simulated and real-world tasks, we demonstrate that LLaRA achieves state-of-the-art performance while preserving the generalization capabilities of large language models. The code, datasets, and pretrained models are available at https://github.com/LostXine/LLaRA.
Towards hyperparameter-free optimization with differential privacy
Authors: Ruixuan Liu; Zhiqi Bu
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Differential privacy (DP) is a privacy-preserving paradigm that protects the training data when training deep learning models. Critically, the performance of models is determined by the training hyperparameters, especially those of the learning rate schedule, thus requiring fine-grained hyperparameter tuning on the data. In practice, it is common to tune the learning rate hyperparameters through the grid search that (1) is computationally expensive as multiple runs are needed, and (2) increases the risk of data leakage as the selection of hyperparameters is data-dependent. In this work, we adapt the automatic learning rate schedule to DP optimization for any models and optimizers, so as to significantly mitigate or even eliminate the cost of hyperparameter tuning when applied together with automatic per-sample gradient clipping. Our hyperparameter-free DP optimization is almost as computationally efficient as the standard non-DP optimization, and achieves state-of-the-art DP performance on various language and vision tasks.
Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning
Authors: Bahare Fatemi; Mehran Kazemi; Anton Tsitsulin; Karishma Malkan; Jinyeong Yim; John Palowitch; Sungyong Seo; Jonathan Halcrow; Bryan Perozzi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex temporal logic. Existing research has explored LLM performance on temporal reasoning using diverse datasets and benchmarks. However, these studies often rely on real-world data that LLMs may have encountered during pre-training or employ anonymization techniques that can inadvertently introduce factual inconsistencies. In this work, we address these limitations by introducing novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. The diversity of question types across these datasets enables systematic investigation into the impact of the problem structure, size, question type, fact order, and other factors on LLM performance. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks. To foster further research in this area, we will open-source the datasets and evaluation framework used in our experiments.
Efficient stagewise pretraining via progressive subnetworks
Authors: Abhishek Panigrahi; Nikunj Saunshi; Kaifeng Lyu; Sobhan Miryoosefi; Sashank J. Reddi; Satyen Kale; Sanjiv Kumar
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent developments in large language models have sparked interest in efficient
pretraining methods. Stagewise training approaches to improve efficiency, like
gradual stacking and layer dropping (Reddi et al., 2023; Zhang & He, 2020), have
recently garnered attention. The prevailing view suggests that stagewise dropping
strategies, such as layer dropping, are ineffective, especially when compared to
stacking-based approaches. This paper challenges this notion by demonstrating
that, with proper design, dropping strategies can be competitive, if not better, than
stacking methods. Specifically, we develop a principled stagewise training framework, progressive subnetwork training, which only trains subnetworks within the
model and progressively increases the size of subnetworks during training, until it
trains the full network. We propose an instantiation of this framework — Random
Part Training (RAPTR) — that selects and trains only a random subnetwork (e.g.
depth-wise, width-wise) of the network at each step, progressively increasing the
size in stages. We show that this approach not only generalizes prior works like
layer dropping but also fixes their key issues. Furthermore, we establish a theoretical basis for such approaches and provide justification for (a) increasing complexity of subnetworks in stages, conceptually diverging from prior works on layer
dropping, and (b) stability in loss across stage transitions in presence of key modern architecture components like residual connections and layer norms. Through
comprehensive experiments, we demonstrate that RAPTR can significantly speed
up training of standard benchmarks like BERT and UL2, up to 33% compared to
standard training and, surprisingly, also shows better downstream performance on
UL2, improving QA tasks and SuperGLUE by 1.5%; thereby, providing evidence
of better inductive bias.
AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials
Authors: Yiheng Xu; Dunjie Lu; Zhennan Shen; Junli Wang; Zekun Wang; Yuchen Mao; Caiming Xiong; Tao Yu
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Graphical User Interface (GUI) agents hold great potential for automating complex tasks across diverse digital environments, from web applications to desktop software. However, the development of such agents is hindered by the lack of high-quality, multi-step trajectory data required for effective training. Existing approaches rely on expensive and labor-intensive human annotation, making them unsustainable at scale. To address this challenge, we propose AgentTrek, a scalable data synthesis pipeline that generates high-quality web agent trajectories by leveraging web tutorials. Our method automatically gathers tutorial-like texts from the internet, transforms them into task goals with step-by-step instructions, and employs a visual-language model (VLM) agent to simulate their execution in a real digital environment. A VLM-based evaluator ensures the correctness of the generated trajectories. We demonstrate that training GUI agents with these synthesized trajectories significantly improves their grounding and planning performance over the current models. Moreover, our approach is more cost-efficient compared to traditional human annotation methods. This work underscores the potential of guided replay with web tutorials as a viable strategy for large-scale GUI agent training, paving the way for more capable and autonomous digital agents.
Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback
Authors: Sanjiban Choudhury; Paloma Sodhi
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: While large language models (LLMs) show impressive decision-making abilities, current methods lack a mechanism for automatic self-improvement from errors during task execution. We propose LEAP, an iterative fine-tuning framework that continually improves LLM agents using feedback from AI expert teachers. Our key insight is to equip the expert teachers with a privileged state -- information available during training but hidden at test time. This allows even weak experts to provide precise guidance, significantly improving the student agent's performance without access to privileged information at test time.
We evaluate LEAP on multiple decision-making benchmarks, including text-based games (ALFWorld), web navigation (WebShop), and interactive coding (Intercode Bash). Our experiments show that LEAP (1) outperforms behavior cloning and ReAct baselines (2) enables weak student models (e.g., Llama3-8B) to exceed performance of strong teacher models (GPT-4o), and (3) allows weak models to self-improve using privileged versions of themselves. 
We provide a theoretical analysis showing that LEAP's success hinges on balancing privileged information with student’s realizability, which we empirically validate. Our code is available at \url{https://leap-llm.github.io}.
Progressive distillation induces an implicit curriculum
Authors: Abhishek Panigrahi; Bingbin Liu; Sadhika Malladi; Andrej Risteski; Surbhi Goel
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Knowledge distillation leverages a teacher model to improve the training of a student model. A persistent challenge is that a better teacher does not always yield a better student, to which a common mitigation is to use additional supervision from several “intermediate” teachers. One empirically validated variant of this principle is progressive distillation, where the student learns from successive intermediate checkpoints of the teacher. Using sparse parity as a sandbox, we identify an implicit curriculum as one mechanism through which progressive distillation accelerates the student’s learning. This curriculum is available only through the intermediate checkpoints but not the final converged one, and imparts both empirical acceleration and a provable sample complexity benefit to the student. We then extend our investigation to Transformers trained on probabilistic context-free grammars (PCFGs) and real-world pre-training datasets (Wikipedia and Books). Through probing the teacher model, we identify an analogous implicit curriculum where the model progressively learns features that capture longer context. Our theoretical and empirical findings on sparse parity, complemented by empirical observations on more complex tasks, highlight the benefit of progressive distillation via implicit curriculum across setups.
MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding
Authors: Fei Wang; Xingyu Fu; James Y. Huang; Zekun Li; Qin Liu; Xiaogeng Liu; Mingyu Derek Ma; Nan Xu; Wenxuan Zhou; Kai Zhang; Tianyi Lorena Yan; Wenjie Jacky Mo; Hsiang-Hui Liu; Pan Lu; Chunyuan Li; Chaowei Xiao; Kai-Wei Chang; Dan Roth; Sheng Zhang; Hoifung Poon; Muhao Chen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We introduce MuirBench, a comprehensive benchmark that focuses on robust multi-image understanding capabilities of multimodal LLMs. MuirBench consists of 12 diverse multi-image tasks (e.g., scene understanding, ordering) that involve 10 categories of multi-image relations (e.g., multiview, temporal relations). Comprising 11,264 images and 2,600 multiple-choice questions, MuirBench is created in a pairwise manner, where each standard instance is paired with an unanswerable variant that has minimal semantic differences, in order for a reliable assessment. Evaluated upon 20 recent multi-modal LLMs, our results reveal that even the best-performing models like GPT-4o and Gemini Pro find it challenging to solve MuirBench, achieving 68.0% and 49.3% in accuracy. Open-source multimodal LLMs trained on single images can hardly generalize to multi-image questions, hovering below 33.3% in accuracy. These results highlight the importance of MuirBench in encouraging the community to develop multimodal LLMs that can look beyond a single image, suggesting potential pathways for future improvements.
Online Clustering with Nearly Optimal Consistency
Authors: T-H. Hubert Chan; Shaofeng H.-C. Jiang; Tianyi Wu; Mengshi Zhao
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We give online algorithms for $k$-Means(more generally, $(k, z)$-Clustering) with nearly optimal consistency (a notion suggested by Lattanzi & Vassilvitskii (2017)). 
Our result turns any $\alpha$-approximate offline algorithm for clustering into an $(1+\epsilon)\alpha^2$-competitive online algorithm for clustering with $O(k \text{poly} \log n)$ consistency. 
This consistency bound is optimal up to $\text{poly} \log(n)$ factors. 
Plugging in the offline algorithm that returns the exact optimal solution, 
we obtain the first
$(1 + \epsilon)$-competitive online algorithm for clustering that achieves a linear in $k$ consistency.
This simultaneously improves several previous results (Lattanzi & Vassilvitskii, 2017; Fichtenberger et al., 2021). 
We validate the performance of our algorithm on real datasets by plugging in the practically efficient $k$-Means++ algorithm. 
Our online algorithm makes $k$-Means++ achieve good consistency with little overhead to the quality of solutions.
Graph Transformers Dream of Electric Flow
Authors: Xiang Cheng; Lawrence Carin; Suvrit Sra
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We show theoretically and empirically that the linear Transformer, when applied to graph data, can implement algorithms that solve canonical problems such as electric flow and eigenvector decomposition. The Transformer has access to information on the input graph only via the graph's incidence matrix. We present explicit weight configurations for implementing each algorithm, and we bound the constructed Transformers' errors by the errors of the underlying algorithms. Our theoretical findings are corroborated by experiments on synthetic data. Additionally, on a real-world molecular regression task, we observe that the linear Transformer is capable of learning a more effective positional encoding than the default one based on Laplacian eigenvectors. Our work is an initial step towards elucidating the inner-workings of the Transformer for graph data. Code is available at https://github.com/chengxiang/LinearGraphTransformer
Doubly Optimal Policy Evaluation for Reinforcement Learning
Authors: Shuze Liu; Claire Chen; Shangtong Zhang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Policy evaluation estimates the performance of a policy by (1) collecting data from the environment and (2) processing raw data into a meaningful estimate. Due to the sequential nature of reinforcement learning, any improper data-collecting policy or data-processing method substantially deteriorates the variance of evaluation results over long time steps. Thus, policy evaluation often suffers from large variance and requires massive data to achieve the desired accuracy. In this work, we design an optimal combination of data-collecting policy and data-processing baseline. Theoretically, we prove our doubly optimal policy evaluation method is unbiased and guaranteed to have lower variance than previously best-performing methods. Empirically, compared with previous works, we show our method reduces variance substantially and achieves superior empirical performance.