GenAI4ELab/papercli-papers huggingface dataset:
Can Large Language Models Understand Symbolic Graphics Programs?
Authors: Zeju Qiu; Weiyang Liu; Haiwen Feng; Zhen Liu; Tim Z. Xiao; Katherine M. Collins; Joshua B. Tenenbaum; Adrian Weller; Michael J. Black; Bernhard Schölkopf
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Against the backdrop of enthusiasm for large language models (LLMs), there is a growing need to scientifically assess their capabilities and shortcomings. This is nontrivial in part because it is difficult to find tasks which the models have not encountered during training. Utilizing symbolic graphics programs, we propose a domain well-suited to test multiple spatial-semantic reasoning skills of LLMs. Popular in computer graphics, these programs procedurally generate visual data. While LLMs exhibit impressive skills in general program synthesis and analysis, symbolic graphics programs offer a new layer of evaluation: they allow us to test an LLM's ability to answer semantic questions about the images or 3D geometries without a vision encoder. To semantically understand the symbolic programs, LLMs would need to possess the ability to "imagine" and reason how the corresponding graphics content would look with only the symbolic description of the local curvatures and strokes. We use this task to evaluate LLMs by creating a large benchmark for the semantic visual understanding of symbolic graphics programs, built procedurally with minimal human effort. Particular emphasis is placed on transformations of images that leave the image level semantics invariant while introducing significant changes to the underlying program. We evaluate commercial and open-source LLMs on our benchmark to assess their ability to reason about visual output of programs, finding that LLMs considered stronger at reasoning generally perform better. Lastly, we introduce a novel method to improve this ability -- Symbolic Instruction Tuning (SIT), in which the LLM is finetuned with pre-collected instruction data on symbolic graphics programs. Interestingly, we find that SIT not only improves LLM's understanding on symbolic programs, but it also improves general reasoning ability on various other benchmarks.
Steering Large Language Models between Code Execution and Textual Reasoning
Authors: Yongchao Chen; Harsh Jhamtani; Srinagesh Sharma; Chuchu Fan; Chi Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: While a lot of recent research focuses on enhancing the textual reasoning capabilities of Large Language Models (LLMs) by optimizing the multi-agent framework or reasoning chains, several benchmark tasks can be solved with 100\% success through direct coding, which is more scalable and avoids the computational overhead associated with textual iterating and searching. Textual reasoning has inherent limitations in solving tasks with challenges in math, logics, optimization, and searching, which is unlikely to be solved by simply scaling up the model and data size. The recently released OpenAI GPT Code Interpreter and multi-agent frameworks such as AutoGen have demonstrated remarkable proficiency of integrating code generation and execution to solve complex tasks using LLMs. However, based on our experiments on 7 existing popular methods for steering code/text generation in both single- and multi-turn settings with 14 tasks and 6 types of LLMs (including the new O1-preview), currently there is no optimal method to correctly steer LLMs to write code when needed. We discover some interesting patterns on when models use code vs. textual reasoning with the evolution to task complexity and model sizes, which even result in an astonishingly inverse scaling behavior. We also discover that results from LLM written code are not always better than using textual reasoning, even if the task could be solved through code. To mitigate the above issues, we propose three methods to better steer LLM code/text generation and achieve a notable improvement. The costs of token lengths and runtime are thoroughly discussed for all the methods. We believe the problem of steering LLM code/text generation is critical for future research and has much space for further improvement. Project Page, Datasets, and Codes are available at https://yongchao98.github.io/CodeSteer/.
Misspecified  $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
Authors: Ally Yalei Du; Lin Yang; Ruosong Wang
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The recent work by Dong and Yang (2023) showed for misspecified sparse linear bandits, one can obtain an $O(\epsilon)$-optimal policy using a polynomial number of samples when the sparsity is a constant, where $\epsilon$ is the misspecification error. This result is in sharp contrast to misspecified linear bandits without sparsity, which require an exponential number of samples to get the same guarantee. In order to study whether the analog result is possible in the reinforcement learning setting, we consider the following problem: assuming the optimal $Q$-function is a $d$-dimensional linear function with sparsity $k$ and misspecification error $\epsilon$, whether we can obtain an $O(\epsilon)$-optimal policy using number of samples polynomially in the feature dimension $d$. We first demonstrate why the standard approach based on Bellman backup or the existing optimistic value function elimination approach such as OLIVE (Jiang et al., 2017) achieves suboptimal guarantees for this problem. We then design a novel elimination-based algorithm to show one can obtain an 
$O(H\epsilon)$-optimal policy with sample complexity polynomially in the feature dimension $d$ and planning horizon $H$. Lastly, we complement our upper bound with an $\tilde \Omega(H\epsilon)$ suboptimality lower bound, giving a complete picture of this problem.
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
Authors: Tanya Chowdhury; Yair Zick; James Allan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Numerous works propose post-hoc, model-agnostic explanations for learning to rank, focusing on ordering entities by their relevance to a query through feature attribution methods. However, these attributions often weakly correlate or contradict each other, confusing end users.  We adopt an axiomatic game-theoretic approach, popular in the feature attribution community, to identify a set of fundamental axioms that every ranking-based feature attribution method should satisfy. We then introduce Rank-SHAP, extending classical Shapley values to ranking. We evaluate the RankSHAP framework through extensive experiments on two datasets, multiple ranking methods and evaluation metrics. Additionally, a user study confirms RankSHAP’s alignment with human intuition. We also perform an axiomatic analysis of existing rank attribution algorithms to determine their compliance with our proposed axioms. Ultimately, our aim is to equip practitioners with a set of axiomatically backed feature attribution methods for studying IR ranking models, that ensure generality as well as consistency.
Machine Unlearning Fails to Remove Data Poisoning Attacks
Authors: Martin Pawelczyk; Jimmy Z. Di; Yiwei Lu; Gautam Kamath; Ayush Sekhari; Seth Neel
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to remove the effects of poisoned data. We experimentally demonstrate that, while existing unlearning methods have been demonstrated to be effective in a number of settings, they fail to remove the effects of data poisoning across a variety of types of poisoning attacks (indiscriminate, targeted, and a newly-introduced Gaussian poisoning attack) and models (image classifiers and LLMs); even when granted a relatively large compute budget. In order to precisely characterize unlearning efficacy, we introduce new evaluation metrics for unlearning based on data poisoning. Our results suggest that a broader perspective, including a wider variety of evaluations, are required to avoid a false sense of confidence in machine unlearning procedures for deep learning without provable guarantees. Moreover, while unlearning methods show some signs of being useful to efficiently remove poisoned data without having to retrain, our work suggests that these methods are not yet ``ready for prime time,'' and currently provide limited benefit over retraining.
Residual-MPPI: Online Policy Customization for Continuous Control
Authors: Pengcheng Wang; Chenran Li; Catherine Weaver; Kenta Kawamoto; Masayoshi Tomizuka; Chen Tang; Wei Zhan
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Policies developed through Reinforcement Learning (RL) and Imitation Learning (IL) have shown great potential in continuous control tasks, but real-world applications often require adapting trained policies to unforeseen requirements. While fine-tuning can address such needs, it typically requires additional data and access to the original training metrics and parameters.
In contrast, an online planning algorithm, if capable of meeting the additional requirements, can eliminate the necessity for extensive training phases and customize the policy without knowledge of the original training scheme or task. In this work, we propose a generic online planning algorithm for customizing continuous-control policies at the execution time, which we call Residual-MPPI. It can customize a given prior policy on new performance metrics in few-shot and even zero-shot online settings, given access to the prior action distribution alone. Through our experiments, we demonstrate that the proposed Residual-MPPI algorithm can accomplish the few-shot/zero-shot online policy customization task effectively, including customizing the champion-level racing agent, Gran Turismo Sophy (GT Sophy) 1.0, in the challenging car racing scenario, Gran Turismo Sport (GTS) environment. Code for MuJoCo experiments is included in the supplementary and will be open-sourced upon acceptance. Demo videos are available on our website: https://sites.google.com/view/residual-mppi.
MCNC: Manifold-Constrained Reparameterization for Neural Compression
Authors: Chayne Thrash; Reed Andreas; Ali Abbasi; Parsa Nooralinejad; Soroush Abbasi Koohpayegani; Hamed Pirsiavash; Soheil Kolouri
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The outstanding performance of large foundational models across diverse tasks,
from computer vision to speech and natural language processing, has significantly
increased their demand. However, storing and transmitting these models poses
significant challenges due to their massive size (e.g., 750GB for Llama 3.1 405B).
Recent literature has focused on compressing the original weights or reducing the
number of parameters required for fine-tuning these models. These compression
methods generally constrain the parameter space, for example, through low-rank
reparametrization (e.g., LoRA), pruning, or quantization (e.g., QLoRA) during
or after the model training. In this paper, we present a novel model compres-
sion method, which we term Manifold-Constrained Neural Compression (MCNC).
This method constrains the parameter space to low-dimensional pre-defined and
frozen nonlinear manifolds, which effectively cover this space. Given the preva-
lence of good solutions in over-parameterized deep neural networks, we show that
by constraining the parameter space to our proposed manifold, we can identify
high-quality solutions while achieving unprecedented compression rates across
a wide variety of tasks and architectures. Through extensive experiments in
computer vision and natural language processing tasks, we demonstrate that our
method significantly outperforms state-of-the-art baselines in terms of compres-
sion, accuracy, and/or model reconstruction time. Our code is publicly available at
https://github.com/mint-vu/MCNC.
HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing
Authors: Mude Hui; Siwei Yang; Bingchen Zhao; Yichun Shi; Heng Wang; Peng Wang; Cihang Xie; Yuyin Zhou
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data.
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Authors: Yang Cai; Gabriele Farina; Julien Grand-Clément; Christian Kroer; Chung-Wei Lee; Haipeng Luo; Weiqiang Zheng
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: We study last-iterate convergence properties of algorithms for solving two-player zero-sum games based on Regret Matching$^+$ (RM$^+$). Despite their widespread use for solving real games, virtually nothing is known about their last-iterate convergence. A major obstacle to analyzing RM-type dynamics is that their regret operators lack Lipschitzness and (pseudo)monotonicity.
We start by showing numerically that several variants used in practice, such as RM$^+$, predictive RM$^+$ and alternating RM$^+$, all lack last-iterate convergence guarantees even on a simple $3\times 3$ matrix game.
We then prove that recent variants of these algorithms based on a smoothing technique, extragradient RM$^{+}$ and smooth Predictive RM$^+$,  enjoy asymptotic last-iterate convergence (without a rate), $1/\sqrt{t}$ best-iterate convergence, and when combined with restarting, linear-rate last-iterate convergence. Our analysis builds on a new characterization of the geometric structure of the limit points of our algorithms, marking a significant departure from most of the literature on last-iterate convergence. We believe that our analysis may be of independent interest and offers a fresh perspective for studying last-iterate convergence in algorithms based on non-monotone operators.
STAR: Synthesis of Tailored Architectures
Authors: Armin W Thomas; Rom Parnichkun; Alexander Amini; Stefano Massaroli; Michael Poli
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Iterative improvement of model architectures is fundamental to deep learning: Transformers first enabled scaling, and recent advances in model hybridization have pushed the quality-efficiency frontier. However, optimizing architectures remains challenging and expensive, with a variety of automated or manual approaches that fall short, due to limited progress in the design of search spaces and due to the simplicity of resulting patterns and heuristics. In this work, we propose a new approach for the synthesis of tailored architectures (STAR). Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics. Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling.
HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics
Authors: Jingxuan Fan; Sarah Martinson; Erik Y. Wang; Kaylie Hausknecht; Jonah Brenner; Danxian Liu; Nianli Peng; Corey Wang; Michael Brenner
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Advanced applied mathematics problems are underrepresented in existing Large Language Model (LLM) benchmark datasets. To address this, we introduce $\textbf{HARDMath}$, a dataset inspired by a graduate course on asymptotic methods, featuring challenging applied mathematics problems that require analytical approximation techniques. These problems demand a combination of mathematical reasoning, computational tools, and subjective judgment, making them difficult for LLMs. Our framework auto-generates a large number of problems with solutions validated against numerical ground truths. We evaluate both open- and closed-source LLMs on $\textbf{HARDMath-mini}$, a sub-sampled test set of 366 problems, as well as on 40 word problems formulated in applied science contexts. Even leading closed-source models like GPT-4 achieve only 43.8% overall accuracy with few-shot Chain-of-Thought prompting, and all models demonstrate significantly lower performance compared to results on existing mathematics benchmark datasets. We additionally conduct a detailed error analysis to gain insights into the failure cases of LLMs. These results demonstrate the limitations of current LLM performance on advanced graduate-level applied math problems and underscore the importance of datasets like $\textbf{HARDMath}$ to advance mathematical abilities of LLMs.
Provable weak-to-strong generalization via benign overfitting
Authors: David Xing Wu; Anant Sahai
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: The classic teacher-student model in machine learning posits that a strong teacher supervises a weak student to improve the student's capabilities.
    We instead consider the inverted situation, where a weak teacher supervises a strong student with imperfect pseudolabels. 
    This paradigm was recently brought forth by \citet{burns2023weak} and termed \emph{weak-to-strong generalization}. 
    We theoretically investigate weak-to-strong generalization for binary and multilabel classification in a stylized overparameterized spiked covariance model with Gaussian covariates where the weak teacher's pseudolabels are asymptotically like random guessing.
    Under these assumptions, we provably identify two asymptotic phases of the strong student's generalization after weak supervision: (1) successful generalization and (2) random guessing. 
    Our techniques should eventually extend to weak-to-strong multiclass classification. 
    Towards doing so, we prove a tight lower tail inequality for the maximum of correlated Gaussians, which may be of independent interest.
    Understanding the multilabel setting reinforces the value of using logits for weak supervision when they are available.
Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models
Authors: Javier Ferrando; Oscar Balcells Obeso; Senthooran Rajamanoharan; Neel Nanda
Venue: ICLR 2025 (ICLR 2025 Oral)

Abstract: Hallucinations in large language models are a widespread problem, yet the mechanisms behind whether models will hallucinate are poorly understood, limiting our ability to solve this problem. Using sparse autoencoders as an interpretability tool, we discover that a key part of these mechanisms is entity recognition, where the model detects if an entity is one it can recall facts about. Sparse autoencoders uncover meaningful directions in the representation space, these detect whether the model recognizes an entity, e.g. detecting it doesn't know about an athlete or a movie. This shows that models can have self-knowledge: internal representations about their own capabilities. These directions are causally relevant: capable of steering the model to refuse to answer questions about known entities, or to hallucinate attributes of unknown entities when it would otherwise refuse. We demonstrate that despite the sparse autoencoders being trained on the base model, these directions have a causal effect on the chat model's refusal behavior, suggesting that chat finetuning has repurposed this existing mechanism. Furthermore, we provide an initial exploration into the mechanistic role of these directions in the model, finding that they disrupt the attention of downstream heads that typically move entity attributes to the final token.
Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization
Authors: Tianxu Li; Kun Zhu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent works have increasingly focused on learning decentralized policies for agents as a solution to the scalability challenges in Multi-Agent Reinforcement Learning (MARL), where agents typically share the parameters of a policy network to make action decisions. However, this parameter sharing can impede efficient exploration, as it may lead to similar behaviors among agents. Different from previous mutual information-based methods that promote multi-agent diversity, we introduce a novel multi-agent exploration method called Trajectory Entropy Exploration (TEE). Our method employs a particle-based entropy estimator to maximize the entropy of different agents' trajectories in a contrastive trajectory representation space, resulting in diverse trajectories and efficient exploration. This entropy estimator avoids challenging density modeling and scales effectively in high-dimensional multi-agent settings. We integrate our method with MARL algorithms by deploying an intrinsic reward for each agent to encourage entropy maximization. To validate the effectiveness of our method, we test our method in challenging multi-agent tasks from several MARL benchmarks. The results demonstrate that our method consistently outperforms existing state-of-the-art methods.
Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction
Authors: Ziyang Wu; Tianjiao Ding; Yifu Lu; Druv Pai; Jingyuan Zhang; Weida Wang; Yaodong Yu; Yi Ma; Benjamin David Haeffele
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However, transformer attention operators often impose a significant computational burden, with the computational complexity scaling quadratically with the number of tokens. In this work, we propose a novel transformer attention operator whose computational complexity scales linearly with the number of tokens. We derive our network architecture by extending prior work which has shown that a transformer style architecture naturally arises by "white-box" architecture design, where each layer of the network is designed to implement an incremental optimization step of a maximal coding rate reduction objective (MCR$^2$). Specifically, we derive a novel variational form of the MCR$^2$ objective and show that the architecture that results from unrolled gradient descent of this variational objective leads to a new attention module called Token Statistics Self-Attention ($\texttt{TSSA}$). $\texttt{TSSA}$ has $\textit{linear computational and memory complexity}$ and radically departs from the typical attention architecture that computes pairwise similarities between tokens. Experiments on vision, language, and long sequence tasks show that simply swapping $\texttt{TSSA}$ for standard self-attention, which we refer to as the Token Statistics Transformer ($\texttt{ToST}$), achieves competitive performance with conventional transformers while being significantly more computationally efficient and interpretable.  Our results also somewhat call into question the conventional wisdom that pairwise similarity style attention mechanisms are critical to the success of transformer architectures.