Yancy567/AIS-Bench huggingface dataset:
Conference: ICLR 2025

Published as a conference paper at ICLR 2025
REVEALING AND MITIGATING OVER-ATTENTION IN
KNOWLEDGE EDITING
Pinzheng Wang1,2
Zecheng Tang1,2
Keyan Zhou1,2
Juntao Li1,2∗
Qiaoming Zhu1,2
Min Zhang1,2
1School of Computer Science and Technology, Soochow University
2Key Laboratory of Data Intelligence and Advanced Computing, Soochow University
{pzwang1,zctang,kyzhou}@stu.sud.edu.cn
{ljt,qmzhu,minzhang}@suda.edu.cn
ABSTRACT
Large Language Models (LLMs) have demonstrated superior performance across
a wide range of tasks, but they still exhibit undesirable errors due to incorrect
knowledge learned from the training data. To avoid this, knowledge editing methods
emerged to precisely edit the specific model knowledge via efficiently modifying
a very small percentage of parameters. However, those methods can lead to the
problem of Specificity Failure, where the existing knowledge and capabilities are
severely degraded due to editing. Our preliminary indicates that Specificity Failure
primarily stems from the model’s attention heads assigning excessive attention
scores to entities related to the edited knowledge, thereby unduly focusing on
specific snippets within the context, which we denote as the Attention Drift
phenomenon. To mitigate such Attention Drift issue, we introduce a simple yet
effective method Selective Attention Drift Restriction (SADR), which introduces
an additional regularization term during the knowledge editing process to restrict
changes in the attention weight distribution, thereby preventing undue focus on the
edited entity. Experiments on five frequently used strong LLMs demonstrate the
effectiveness of our method, where SADR can significantly mitigate Specificity
Failure in the predominant knowledge editing tasks1.
1
INTRODUCTION
Large language models (LLMs) have demonstrated outstanding performance on various downstream
natural language processing tasks, e.g., dialogue generation (Ni et al., 2023; Yang et al., 2024a),
attributed to the numerous inherent...
Conference: ICML 2025

STACEY: Promoting Stochastic Steepest Descent via Accelerated
ℓp-Smooth Nonconvex Optimization
Xinyu Luo * 1 Cedar Site Bai * 1 Bolian Li * 1 Petros Drineas 1 Ruqi Zhang 1 Brian Bullins 1
Abstract
While popular optimization methods such as
SGD, AdamW, and Lion depend on steepest de-
scent updates in either ℓ2 or ℓ∞norms, there re-
mains a critical gap in handling the non-Euclidean
structure observed in modern deep networks train-
ing. In this work, we address this need by intro-
ducing a new accelerated ℓp steepest descent al-
gorithm, called STACEY, which uses interpolated
primal-dual iterate sequences to effectively nav-
igate non-Euclidean smooth optimization tasks.
In addition to providing novel theoretical guar-
antees for the foundations of our algorithm, we
empirically compare our approach against these
popular methods on tasks including image classi-
fication and language model (LLM) pretraining,
demonstrating both faster convergence and higher
final accuracy. We further evaluate different val-
ues of p across various models and datasets, un-
derscoring the importance and efficiency of non-
Euclidean approaches over standard Euclidean
methods.
Code can be found at https://
github.com/xinyuluo8561/Stacey.
1. Introduction
Stochastic first-order methods have proven essential for effi-
ciently training modern deep learning models. Beyond the
basic stochastic gradient descent (SGD) algorithm (Robbins
& Monro, 1951) and its momentum-based variants (Nes-
terov, 1983; Polyak, 1964), a variety of adaptive methods
have been developed, such as AdaGrad (Duchi et al., 2011a),
Adam (Kingma & Ba, 2015), and AdamW (Loshchilov &
Hutter, 2019), which incorporate second-moment gradient
information to provide per-coordinate scaling. Meanwhile,
*Equal contribution
1Department of Computer Science, Pur-
due University, Indiana, USA. Correspondence to: Xinyu Luo
<luo466@purdue.edu>, Cedar Site Bai <bai123@purdue.edu>,
Bolian Li <li4468@purdue.edu>.
Proceedings of the 42 nd Internat...
Conference: ICML 2025

Riemannian Diffusion Adaptation for Distributed Optimization on Manifolds
Xiuheng Wang 1 Ricardo Borsoi 1 C´edric Richard 2 Ali H. Sayed 3
Abstract
Online distributed optimization is particularly use-
ful for solving optimization problems with stream-
ing data collected by multiple agents over a net-
work. When the solutions lie on a Riemannian
manifold, such problems become challenging to
solve, particularly when efficiency and continuous
adaptation are required. This work tackles these
challenges and devises a diffusion adaptation strat-
egy for decentralized optimization over general
manifolds. A theoretical analysis shows that the
proposed algorithm is able to approach network
agreement after sufficient iterations, which allows
a non-asymptotic convergence result to be derived.
We apply the algorithm to the online decentral-
ized principal component analysis problem and
Gaussian mixture model inference. Experimental
results with both synthetic and real data illustrate
its performance.
1. Introduction
In the decentralized setting, this work deals with the multi-
agent optimization problem seeking consensus on a Rieman-
nian manifold M:
min
w∈M
1
K
K
X
k=1
Jk(w) ,
(1)
where Jk : M →R is a local risk function defined for each
agent by Jk(w) = Exk

Q(w; xk)
	
in terms of the expec-
tation of some loss function Q(w; xk). The computation
of Jk(w) is over the unknown distribution of the data xk,
which makes it necessary to use a stochastic approximation
for the gradient vector based on a set of independent realiza-
tions xk,t, observed sequentially over time. A wide range
Part of this work was done while Xiuheng Wang was a PhD
student at Universit´e Cˆote d’Azur. 1Universit´e de Lorraine, CNRS,
CRAN, France 2Universit´e Cˆote d’Azur, CNRS, OCA, France
3 ´Ecole Polytechnique F´ed´erale de Lausanne, Switzerland. Corre-
spondence to: Xiuheng Wang <dr.xiuheng.wang@gmail.com>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 26...
Conference: ICML 2025

Non-Stationary Predictions May Be More Informative:
Exploring Pseudo-Labels with a Two-Phase Pattern of Training Dynamics
Hongbin Pei 1 Jingxin Hai 1 Yu Li 1 Huiqi Deng 1
Denghao Ma 2 Jie Ma 1 Pinghui Wang 1 Jing Tao 1 Xiaohong Guan 1
Abstract
Pseudo-labeling is a widely used strategy in semi-
supervised learning. Existing methods typically
select predicted labels with high confidence scores
and high training stationarity, as pseudo-labels to
augment training sets. In contrast, this paper ex-
plores the pseudo-labeling potential of predicted
labels that do not exhibit these characteristics. We
discover a new type of predicted labels suitable for
pseudo-labeling, termed two-phase labels, which
exhibit a two-phase pattern during training: they
are initially predicted as one category in early
training stages and switch to another category in
subsequent epochs. Case studies show the two-
phase labels are informative for decision bound-
aries. To effectively identify the two-phase labels,
we design a 2-phasic metric that mathematically
characterizes their spatial and temporal patterns.
Furthermore, we propose a loss function tailored
for two-phase pseudo-labeling learning, allowing
models not only to learn correct correlations but
also to eliminate false ones. Extensive experi-
ments on eight datasets show that our proposed
2-phasic metric acts as a powerful booster for
existing pseudo-labeling methods by additionally
incorporating the two-phase labels, achieving an
average classification accuracy gain of 1.73% on
image datasets and 1.92% on graph datasets.
1. Introduction
Pseudo-labeling (Lee et al., 2013) is a widely employed strat-
egy in many semi-supervised learning methods, e.g., self-
training (Amini et al., 2025), co-training (Blum & Mitchell,
1998), consistency regularization (Sohn et al., 2020), and
1MOE KLINNS Lab, Xi’an Jiaotong University, China;
2Beijing Information Science and Technology University, China.
Correspondence to: Jing Tao <jtao@mail.xjtu.edu.cn...
Conference: ICML 2025

Integrating Intermediate Layer Optimization and Projected Gradient Descent
for Solving Inverse Problems with Diffusion Models
Yang Zheng 1 Wen Li 1 Zhaoqiang Liu 1
Abstract
Inverse problems (IPs) involve reconstructing sig-
nals from noisy observations. Recently, diffu-
sion models (DMs) have emerged as a powerful
framework for solving IPs, achieving remarkable
reconstruction performance. However, existing
DM-based methods frequently encounter issues
such as heavy computational demands and subop-
timal convergence. In this work, building upon
the idea of the recent work DMPlug (Wang et al.,
2024), we propose two novel methods, DMILO
and DMILO-PGD, to address these challenges.
Our first method, DMILO, employs intermediate
layer optimization (ILO) to alleviate the mem-
ory burden inherent in DMPlug. Additionally,
by introducing sparse deviations, we expand the
range of DMs, enabling the exploration of under-
lying signals that may lie outside the range of
the diffusion model. We further propose DMILO-
PGD, which integrates ILO with projected gra-
dient descent (PGD), thereby reducing the risk
of suboptimal convergence. We provide an intu-
itive theoretical analysis of our approaches under
appropriate conditions and validate their superi-
ority through extensive experiments on diverse
image datasets, encompassing both linear and
nonlinear IPs. Our results demonstrate signifi-
cant performance gains over state-of-the-art meth-
ods, highlighting the effectiveness of DMILO and
DMILO-PGD in addressing common challenges
in DM-based IP solvers.
1. Introduction
Inverse problems (IPs) represent a broad class of challenges
focused on reconstructing degraded signals, particularly
images, from noisy observations. These problems are of
1University of Electronic Science and Technology of China.
Correspondence to: Zhaoqiang Liu <zqliu12@gmail.com>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s...
Conference: ICML 2025

ELMO : Efficiency via Low-precision and Peak Memory Optimization
in Large Output Spaces
Jinbin Zhang * 1 Nasib Ullah * 1 Erik Schultheis 1 2 Rohit Babbar 3
Abstract
Large output spaces, also referred to as Extreme
multilabel classification (XMC), is a setting that
arises, e.g., in large-scale tagging and product-
to-product recommendation, and is characterized
by the number of labels ranging from hundreds
of thousands to millions. This means that the
linear classification head, usually only a tiny
fraction of the overall model, turns into the main
driver for compute and memory demand. Current
state-of-the-art XMC methods predominantly
rely on FP16-FP32 mixed-precision training,
which we show can be unstable, and inefficient
in terms of memory usage and computational
overhead.
Meanwhile, existing low-precision
methods typically retain higher precision for
the classification layer.
In this work, we
propose ELMO, a pure low-precision training
framework for XMC models using BFloat16
and Float8 data types.
By leveraging Ka-
han summation and stochastic rounding, we
demonstrate that XMC models can be effectively
trained entirely in Float8, without relying
on single-precision master weights or tensor
scaling. Low-precision training, combined with
our proposed memory optimizations—gradient
fusion
and
chunking—enables
significant
reductions in GPU memory usage. For example,
we train a 3-million-label XMC model with
only 6.6 GiB of GPU memory,
compared
to the 39.7 GiB required by the optimized
SOTA method, Renee (Jain et al., 2023) with-
out compromising accuracy. Code available at
https://github.com/xmc-aalto/elmo.
*Equal contribution 1Department of Computer Science, Aalto
University, Espoo, Finland 2IST Austria 3Department of Computer
Science, University of Bath, Bath, UK. Correspondence to: Jinbin
Zhang <jinbin.zhang@aalto.fi>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1. Intro...
Conference: NeurIPS 2025

Data Selection Matters: Towards Robust Instruction
Tuning of Large Multimodal Models
Xu Yang1
Chen Liu† 1
Ying Wei† 2
1 City University of Hong Kong
2 Zhejiang University
xyang337-c@my.cityu.edu.hk
chen.liu@cityu.edu.hk
ying.wei@zju.edu.cn
Abstract
Selecting a compact subset of visual instruction–following data has emerged as
an effective way to align large multimodal models with human intentions while
avoiding the high cost of full-dataset training. Yet we observe that both full-data
training and existing state-of-the-art data selection methods tend to inherit underly-
ing dataset biases such as position bias and spurious correlations, leading to biased
model behaviors. To address this issue, we introduce ARDS, a robustness-aware
targeted visual instruction-selection framework that explicitly mitigates these weak-
nesses, sidestepping the need for access to downstream data or time-consuming
gradient computation. Specifically, we first identify the worst-case evaluation sub-
groups through visual and textual task-specific perturbations. The robust training
mixture is then constructed by prioritizing samples that are semantically closer to
these subgroups in a rich multimodal embedding space. Extensive experiments
demonstrate that ARDS substantially boosts both robustness and data efficiency for
visual instruction tuning. We also showcase that the robust mixtures produced with
a smaller model transfer effectively to larger architectures. Our code and selected
datasets that have been demonstrated transferable across models are available at
https://github.com/xyang583/ARDS.
1
Introduction
Large multimodal models (LMMs) [23, 9, 30, 123, 64, 115, 75] have garnered significant attention
due to their strong zero-shot capabilities, enabling a wide range of applications in real-world scenarios.
Visual instruction tuning [64] has proven effective in enhancing these models’ abilities to follow user
instructions and reason deeply based on visual cues. Recent efforts have demons...
Conference: ICML 2025

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Ziyang Yu 1 Wenbing Huang 2 3 Yang Liu 1 4
Abstract
Molecular Dynamics (MD) simulations are essen-
tial for understanding the atomic-level behavior of
molecular systems, giving insights into their tran-
sitions and interactions. However, classical MD
techniques are limited by the trade-off between ac-
curacy and efficiency, while recent deep learning-
based improvements have mostly focused on
single-domain molecules, lacking transferability
to unfamiliar molecular systems. Therefore, we
propose Unified Simulator (UniSim), which lever-
ages cross-domain knowledge to enhance the un-
derstanding of atomic interactions. First, we em-
ploy a multi-head pretraining approach to learn a
unified atomic representation model from a large
and diverse set of molecular data. Then, based on
the stochastic interpolant framework, we learn the
state transition patterns over long timesteps from
MD trajectories, and introduce a force guidance
module for rapidly adapting to different chemical
environments. Our experiments demonstrate that
UniSim achieves highly competitive performance
across small molecules, peptides, and proteins.
1. Introduction
Molecular Dynamics (MD) simulations, an in silico method
used to comprehend the time evolution of molecular sys-
tems in given environments, serve as a fundamental and
essential tool in various fields like computational chem-
istry, pharmacology, material design, and condensed mat-
ter physics (Van Gunsteren & Berendsen, 1990; Lindorff-
Larsen et al., 2011; Hollingsworth & Dror, 2018; Lau et al.,
*Equal contribution
1Department of Computer Science and
Technology, Tsinghua University, Beijing, China 2Gaoling School
of Artificial intelligence, Renmin University of China, Beijing,
China 3Engineering Research Center of Next-Generation Intelli-
gent Search and Recommendation, MOE 4Institute for AI Industry
Research (AIR), Tsinghua University, Beijing, China. Correspon-
dence to...
Conference: ICML 2025

Let LLM Tell What to Prune and How Much to Prune
Mingzhe Yang * 1 Sihao Lin * 2 Changlin Li 3 Xiaojun Chang 1
Abstract
Large language models (LLMs) have revolution-
ized various AI applications. However, their bil-
lions of parameters pose significant challenges
for practical deployment. Structured pruning is
a hardware-friendly compression technique and
receives widespread attention. Nonetheless, exist-
ing literature typically targets a single structure
of LLMs. We observe that the structure units of
LLMs differ in terms of inference cost and func-
tionality. Therefore, pruning a single structure
unit in isolation often results in an imbalance be-
tween performance and efficiency. In addition,
previous works mainly employ a prescribed prun-
ing ratio. Since the significance of LLM modules
may vary, it is ideal to distribute the pruning load
to a specific structure unit according to its role
within LLMs. To address the two issues, we pro-
pose a pruning method that targets multiple LLM
modules with dynamic pruning ratios. Specifi-
cally, we find the intrinsic properties of LLMs
can guide us to determine the importance of each
module and thus distribute the pruning load on de-
mand, i.e., what to prune and how much to prune.
This is achieved by quantifying the complex inter-
actions within LLMs. Extensive experiments on
multiple benchmarks and LLM variants demon-
strate that our method effectively balances the
trade-off between efficiency and performance.
1. Introduction
Large language models (LLMs) have demonstrated excep-
tional performance in the field of natural language process-
ing (Mann et al., 2020; Thoppilan et al., 2022; Zhu et al.,
2024). Nonetheless, the billions of parameters (Zhao et al.,
2023) characteristic of LLMs substantially increase storage
requirements and computational overhead. Therefore, de-
*Equal contribution 1University of Science and Technology of
China 2RMIT University 3Stanford University. Correspondence
to: Xiaojun Chang <xjchang@ustc...
Conference: ICML 2025

CALM: Consensus-Aware Localized Merging for Multi-Task Learning
Kunda Yan * 1 Min Zhang * 2 Sen Cui † 1 Zikun Qu 3 Bo Jiang 2 Feng Liu 4 Changshui Zhang † 1
Abstract
Model merging aims to integrate the strengths of
multiple fine-tuned models into a unified model
while preserving task-specific capabilities. Ex-
isting methods, represented by task arithmetic,
are typically classified into global- and local-
aware methods. However, global-aware meth-
ods inevitably cause parameter interference, while
local-aware methods struggle to maintain the ef-
fectiveness of task-specific details in the merged
model. To address these limitations, we propose
a Consensus-Aware Localized Merging (CALM)
method which incorporates localized informa-
tion aligned with global task consensus, ensur-
ing its effectiveness post-merging. CALM con-
sists of three key components: (1) class-balanced
entropy minimization sampling, providing a
more flexible and reliable way to leverage un-
supervised data; (2) an efficient-aware frame-
work, selecting a small set of tasks for sequential
merging with high scalability; (3) a consensus-
aware mask optimization, aligning localized bi-
nary masks with global task consensus and merg-
ing them conflict-free. Experiments demonstrate
the superiority and robustness of our CALM, sig-
nificantly outperforming existing methods and
achieving performance close to traditional MTL.
1. Introduction
Multi-task learning (MTL) facilitates knowledge transfer
across different tasks through a shared backbone, improving
*Equal contribution † Corresponding author 1Institute for Artifi-
cial Intelligence, Tsinghua University (THUAI), Beijing National
Research Center for Information Science and Technology (BN-
Rist), Department of Automation, Tsinghua University, Beijing,
P.R.China 2East China Normal University (ECNU) 3The Chinese
University of Hong Kong, Shenzhen 4School of Computing and In-
formation Systems, The University of Melbourne. Correspondence
to: Sen Cui <cuis@ma...
Conference: NeurIPS 2025

Online Optimization for Offline Safe
Reinforcement Learning
Yassine Chemingui
Washington State University
yassine.chemingui@wsu.edu
Aryan Deshwal
University of Minnesota
adeshwal@umn.edu
Alan Fern
Oregon State University
alan.fern@oregonstate.edu
Thanh Nguyen-Tang
New Jersey Institute of Technology
thanh.nguyen@njit.edu
Janardhan Rao Doppa
Washington State University
jana.doppa@wsu.edu
Abstract
We study the problem of Offline Safe Reinforcement Learning (OSRL), where the
goal is to learn a reward-maximizing policy from fixed data under a cumulative
cost constraint. We propose a novel OSRL approach that frames the problem as a
minimax objective and solves it by combining offline RL with online optimization
algorithms. We prove the approximate optimality of this approach when integrated
with an approximate offline RL oracle and no-regret online optimization. We
also present a practical approximation that can be combined with any offline RL
algorithm, eliminating the need for offline policy evaluation. Empirical results
on the DSRL benchmark demonstrate that our method reliably enforces safety
constraints under stringent cost budgets, while achieving high rewards. The code is
available at https://github.com/yassineCh/O3SRL.
1
Introduction
Offline reinforcement learning (RL) [Levine et al., 2020] is a powerful paradigm to learn decision-
making policies from logged datasets without the need for additional interaction with the environment.
Offline RL has shown good success in domains including autonomous driving, robotics, control
systems, and black-box optimization [Fang et al., 2022, Lin et al., 2024, Li et al., 2024, Zhan et al.,
2022, Chemingui et al., 2024] where executing exploratory actions is not practical. However, in
safety-critical domains such as healthcare and smart grid, the decision-making agent needs to also
satisfy some cost/safety constraints. Such problems are studied under the sub-area referred to as
offline safe RL (OSRL) where the goal is to learn r...
Conference: ICML 2025

DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities
in Code Large Language Models Under Data Contamination
Simin Chen 1 Pranav Pusarla 1 Baishakhi Ray 1
1Columbia University
Abstract
The rapid advancement of code large language
models (Code LLMs) underscores the critical
need for effective and transparent benchmarking
methods. However, current benchmarking pre-
dominantly relies on publicly available, human-
created datasets. The widespread use of these
static benchmark datasets makes the evaluation
process particularly susceptible to data contam-
ination—an unavoidable consequence of the ex-
tensive data collection processes employed dur-
ing LLM training.
Existing methods for ad-
dressing data contamination typically face sig-
nificant limitations, including reliance on sub-
stantial human effort and difficulty in manag-
ing class imbalances. To overcome these chal-
lenges, we propose DyCodeEval, a novel bench-
marking suite specifically designed to evaluate
Code LLMs under realistic contamination scenar-
ios. Given an initial seed programming problem,
DyCodeEval utilizes multiple agents to system-
atically extract and modify contextual informa-
tion without changing the core logic, generating
semantically equivalent variations. We introduce
a dynamic data generation method and conduct
extensive empirical studies on two seed datasets
involving 18 Code LLMs. The results demon-
strate that DyCodeEval effectively assesses the
reasoning capabilities of Code LLMs under con-
tamination conditions while producing diverse
problem variants, thereby ensuring robust and
consistent benchmarking outcomes. Our project
webpage can be found at this link1.
1Department of Computer Science, Columbia University. Cor-
respondence to: Simin Chen <sc5687@columbia.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
1https://codekaleidoscope.github.io/
dycodeeval.html
1. Introduction
Large ...