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

Inverse Bridge Matching Distillation
Nikita Gushchin 1 2 David Li * 1 3 Daniil Selikhanovych * 1 4 5 Evgeny Burnaev 1 2 Dmitry Baranchuk 4
Alexander Korotin 1 2
Abstract
Learning diffusion bridge models is easy; mak-
ing them fast and practical is an art. Diffusion
bridge models (DBMs) are a promising extension
of diffusion models for applications in image-to-
image translation. However, like many modern
diffusion and flow models, DBMs suffer from
the problem of slow inference. To address it,
we propose a novel distillation technique based
on the inverse bridge matching formulation and
derive the tractable objective to solve it in prac-
tice. Unlike previously developed DBM distil-
lation techniques, the proposed method can dis-
till both conditional and unconditional types of
DBMs, distill models in a one-step generator, and
use only the corrupted images for training. We
evaluate our approach for both conditional and
unconditional types of bridge matching on a wide
set of setups, including super-resolution, JPEG
restoration, sketch-to-image, and other tasks, and
show that our distillation technique allows us to
accelerate the inference of DBMs from 4x to
100x and even provide better generation qual-
ity than used teacher model depending on par-
ticular setup. We provide the code at https:
//github.com/ngushchin/IBMD.
1. Introduction
Diffusion Bridge Models (DBMs) represent a specialized
class of diffusion models designed for data-to-data tasks,
such as image-to-image translation. Unlike standard diffu-
sion models, which operate by mapping noise to data (Ho
et al., 2020; Sohl-Dickstein et al., 2015), DBMs construct
diffusion processes directly between two data distributions
(Peluchetti, 2023a; Liu et al., 2022b; Somnath et al., 2023;
*Equal contribution 1Skolkovo Institute of Science and Technol-
ogy 2Artificial Intelligence Research Institute 3Moscow Institute
of Physics and Technology 4Yandex Research 5HSE University.
Correspondence to: Nikita Gushchin <n.gushchi...
Conference: NeurIPS 2025

Reasoning Planning for Language Models
Bao Nguyen1
Hieu Trung Nguyen1
Ruifeng She2
Xiaojin Fu2
Viet Anh Nguyen1
1 The Chinese University of Hong Kong
2 Huawei Noah’s Ark Lab
nbnguyen@se.cuhk.edu.hk, thnguyen@se.cuhk.edu.hk
she.ruifeng@huawei.com, fuxiaojin32@hotmail.com, nguyen@se.cuhk.edu.hk
Abstract
Selecting an appropriate reasoning method for a given query remains a key chal-
lenge in language model generation. Existing approaches typically generate multi-
ple candidate responses and use an aggregation strategy to select the output answer,
often assuming that more candidate answers yield higher accuracy. We revisit this
assumption through a rigorous theoretical analysis, deriving accuracy bounds for
standard aggregation methods under fixed generation distributions and candidate
sizes. Building on these insights, we introduce EPIC, an Ensemble PlannIng with
Contrastive learning framework to learn a shared representation space that captures
both model reasoning abilities and query-method compatibility. EPIC incorpo-
rates our probability bounds as a regularizer in a utility-driven optimization that
balances accuracy and computational cost. Experiments on diverse mathematical
reasoning tasks show that EPIC consistently selects optimal reasoning methods,
improving accuracy while reducing computational overhead. Our code can be
found at https://github.com/nguyenngocbaocmt02/EPIC.
1
Introduction
Large Language Models (LLMs) have demonstrated remarkable abilities to understand and reason
in human natural language. These advancements have transformed applications, including travel
planning [Xie et al., 2024a], AI teaching platforms [Jin et al., 2024], and human population simula-
tions [Park et al., 2023, Bui et al., 2025]. However, even with a pre-trained LLM, the computational
expense of serving LLM-powered systems remains a significant bottleneck due to the massive scale
of the models [Lin et al., 2024], the quadratic complexity of the attention mechanism [Dao et al....
Conference: ICML 2025

SCENIR: Visual Semantic Clarity through
Unsupervised Scene Graph Retrieval
Nikolaos Chaidos 1 Angeliki Dimitriou 1 Maria Lymperaiou 1 Giorgos Stamou 1
Abstract
Despite the dominance of convolutional and
transformer-based architectures in image-to-
image retrieval, these models are prone to biases
arising from low-level visual features, such as
color. Recognizing the lack of semantic under-
standing as a key limitation, we propose a novel
scene graph-based retrieval framework that em-
phasizes semantic content over superficial im-
age characteristics. Prior approaches to scene
graph retrieval predominantly rely on supervised
Graph Neural Networks (GNNs), which require
ground truth graph pairs driven from image cap-
tions.
However, the inconsistency of caption-
based supervision stemming from variable text en-
codings undermine retrieval reliability. To address
these, we present SCENIR, a Graph Autoencoder-
based unsupervised retrieval framework, which
eliminates the dependence on labeled training
data. Our model demonstrates superior perfor-
mance across metrics and runtime efficiency, out-
performing existing vision-based, multimodal,
and supervised GNN approaches.
We further
advocate for Graph Edit Distance (GED) as a
deterministic and robust ground truth measure
for scene graph similarity, replacing the inconsis-
tent caption-based alternatives for the first time
in image-to-image retrieval evaluation. Finally,
we validate the generalizability of our method
by applying it to unannotated datasets via auto-
mated scene graph generation, while substantially
contributing in advancing state-of-the-art in coun-
terfactual image retrieval. The source code is
available at https://github.com/nickhaidos/scenir-
icml2025.
1Artificial Intelligence and Learning Systems Laboratory, Na-
tional Technical University of Athens. Correspondence to: Niko-
laos Chaidos <nchaidos@ails.ece.ntua.gr>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada....
Conference: ICML 2025

Random Feature Representation Boosting
Nikita Zozoulenko 1 Thomas Cass * 1 Lukas Gonon * 1 2
Abstract
We introduce Random Feature Representation
Boosting (RFRBoost), a novel method for con-
structing deep residual random feature neural net-
works (RFNNs) using boosting theory. RFRBoost
uses random features at each layer to learn the
functional gradient of the network representation,
enhancing performance while preserving the con-
vex optimization benefits of RFNNs. In the case
of MSE loss, we obtain closed-form solutions to
greedy layer-wise boosting with random features.
For general loss functions, we show that fitting
random feature residual blocks reduces to solving
a quadratically constrained least squares problem.
Through extensive numerical experiments on tab-
ular datasets for both regression and classification,
we show that RFRBoost significantly outperforms
RFNNs and end-to-end trained MLP ResNets in
the small- to medium-scale regime where RFNNs
are typically applied. Moreover, RFRBoost offers
substantial computational benefits, and theoretical
guarantees stemming from boosting theory.
1. Introduction
Random feature neural networks (RFNNs) are single-
hidden-layer neural networks where all model parameters
are randomly initialized or sampled, with only the linear
output layer being trained. This approach presents a compu-
tationally efficient alternative to neural networks trained via
stochastic gradient descent (SGD), avoiding the challenges
associated with non-convex optimization and vanishing/-
exploding gradients. Despite their simplicity, RFNNs and
related random feature models have strong provable gen-
eralization guarantees (Rahimi & Recht, 2008b; Rudi &
Rosasco, 2017; Lanthaler & Nelsen, 2023; Cheng et al.,
2023), and have demonstrated state-of-the-art performance
∗Equal last authors.
1Department of Mathematics, Imperial
College London, UK 2School of Computer Science, University of
St. Gallen, Switzerland. Correspondence to: Nikita Zozoulenko
<n.zoz...
Conference: ICML 2025

GRAM: A Generative Foundation Reward Model
for Reward Generalization
Chenglong Wang 1 Yang Gan 1 Yifu Huo 1 Yongyu Mu 1 Qiaozhi He 1 Murun Yang 1 Bei Li 2 Tong Xiao 1 3
Chunliang Zhang 1 Tongran Liu 4 Jingbo Zhu 1 3
Code
Datasets&Models
Abstract
In aligning large language models (LLMs), re-
ward models have played an important role, but
are standardly trained as discriminative models
and rely only on labeled human preference data.
In this paper, we explore methods that train re-
ward models using both unlabeled and labeled
data. Building on the generative models in LLMs,
we develop a generative reward model that is first
trained via large-scale unsupervised learning and
then fine-tuned via supervised learning. We also
show that by using label smoothing, we are in fact
optimizing a regularized pairwise ranking loss.
This result, in turn, provides a new view of train-
ing reward models, which links generative models
and discriminative models under the same class
of training objectives. The outcome of these tech-
niques is a foundation reward model, which can
be applied to a wide range of tasks with little or no
further fine-tuning effort. Extensive experiments
show that this model generalizes well across sev-
eral tasks, including response ranking, reinforce-
ment learning from human feedback, and task
adaptation with fine-tuning, achieving significant
performance improvements over several strong
baseline models.
1. Introduction
Reward models are a fundamental concept in reinforcement
learning and define what an agent optimizes for. For large
language models (LLMs), fine-tuning with reward models
is a common post-training step to align the model outputs
with desired behaviors and objectives. A widely adopted
1School of Computer Science and Engineering, Northeastern
University, Shenyang, China 2Meituan Inc. 3NiuTrans Research,
Shenyang, China 4CAS Key Laboratory of Behavioral Science,
Institute of Psychology, CAS, Beijing, China. Correspondence
to: Chenglong Wang <clwa...
Conference: NeurIPS 2025

How to build a consistency model:
Learning flow maps via self-distillation
Nicholas M. Boffi
Carnegie Mellon University
Michael S. Albergo
Harvard University
Eric Vanden-Eijnden
Courant Institute of Mathematical Sciences
Abstract
Flow-based generative models achieve state-of-the-art sample quality, but require
the expensive solution of a differential equation at inference time. Flow map mod-
els, commonly known as consistency models, encompass many recent efforts to
improve inference-time efficiency by learning the solution operator of this differ-
ential equation. Yet despite their promise, these models lack a unified description
that clearly explains how to learn them efficiently in practice. Here, building on
the methodology proposed in Boffi et al. (2024), we present a systematic algo-
rithmic framework for directly learning the flow map associated with a flow or
diffusion model. By exploiting a relationship between the velocity field underlying
a continuous-time flow and the instantaneous rate of change of the flow map, we
show how to convert any distillation scheme into a direct training algorithm via
self-distillation, eliminating the need for pre-trained teachers. We introduce three
algorithmic families based on different mathematical characterizations of the flow
map: Eulerian, Lagrangian, and Progressive methods, which we show encompass
and extend all known distillation and direct training schemes for consistency mod-
els. We find that the novel class of Lagrangian methods, which avoid both spatial
derivatives and bootstrapping from small steps by design, achieve significantly
more stable training and higher performance than more standard Eulerian and Pro-
gressive schemes. Our methodology unifies existing training schemes under a single
common framework and reveals new design principles for accelerated generative
modeling. Associated code is available at https://github.com/nmboffi/flow-maps.
1
Introduction
Generative models based on dynamical systems, suc...
Conference: NeurIPS 2025

Consistent Sampling and Simulation: Molecular
Dynamics with Energy-Based Diffusion Models
Michael Plainer† 1,2,3,4
Hao Wu† 5
Leon Klein 1
Stephan Günnemann 6
Frank Noé† 1,7,8
1Freie Universität Berlin
2Zuse School ELIZA
3Technische Universität Berlin
4Berlin Institute for the Foundations of Learning and Data 5Shanghai Jiao Tong University
6Technische Universität München
7Rice University
8Microsoft Research AI4Science
Abstract
In recent years, diffusion models trained on equilibrium molecular distributions
have proven effective for sampling biomolecules. Beyond direct sampling, the
score of such a model can also be used to derive the forces that act on molecular
systems. However, while classical diffusion sampling usually recovers the training
distribution, the corresponding energy-based interpretation of the learned score is
often inconsistent with this distribution, even for low-dimensional toy systems. We
trace this inconsistency to inaccuracies of the learned score at very small diffusion
timesteps, where the model must capture the correct evolution of the data distribu-
tion. In this regime, diffusion models fail to satisfy the Fokker–Planck equation,
which governs the evolution of the score. We interpret this deviation as one source
of the observed inconsistencies and propose an energy-based diffusion model with a
Fokker–Planck-derived regularization term to enforce consistency. We demonstrate
our approach by sampling and simulating multiple biomolecular systems, including
fast-folding proteins, and by introducing a state-of-the-art transferable Boltzmann
emulator for dipeptides that supports simulation and achieves improved consistency
and efficient sampling. Our code, model weights, and self-contained JAX and
PyTorch notebooks are available at https://github.com/noegroup/ScoreMD.
1
Introduction
Understanding biochemical systems requires modeling not only static molecular structures but also
their temporal evolution and interactions. Molecular dynamics (MD) si...
Conference: ICML 2025

DRAG: Data Reconstruction Attack using Guided Diffusion
Wa-Kin Lei 1 Jun-Cheng Chen 2 Shang-Tse Chen 1
Abstract
With the rise of large foundation models, split
inference (SI) has emerged as a popular compu-
tational paradigm for deploying models across
lightweight edge devices and cloud servers, ad-
dressing data privacy and computational cost con-
cerns. However, most existing data reconstruction
attacks have focused on smaller CNN classifica-
tion models, leaving the privacy risks of founda-
tion models in SI settings largely unexplored. To
address this gap, we propose a novel data recon-
struction attack based on guided diffusion, which
leverages the rich prior knowledge embedded in
a latent diffusion model (LDM) pre-trained on a
large-scale dataset. Our method performs iterative
reconstruction on the LDM’s learned image prior,
effectively generating high-fidelity images resem-
bling the original data from their intermediate rep-
resentations (IR). Extensive experiments demon-
strate that our approach significantly outperforms
state-of-the-art methods, both qualitatively and
quantitatively, in reconstructing data from deep-
layer IRs of the vision foundation model. The
results highlight the urgent need for more robust
privacy protection mechanisms for large models
in SI scenarios. Code is available at: https:
//github.com/ntuaislab/DRAG
1. Introduction
The rapid development of deep learning has revolution-
ized various aspects of daily life—from AI assistants to
autonomous vehicles. However, the substantial computa-
tional resources required by these emerging models often
hinder their deployment on edge devices. Therefore, offload-
ing intensive computation to cloud servers has become a
popular alternative. Following this paradigm, split inference
(SI) (Kang et al., 2017) has emerged as one of the most
1National Taiwan University 2Research Center for Information
Technology Innovation, Academia Sinica. Correspondence to:
Shang-Tse Chen <stchen@csie.ntu.edu.tw>.
Pr...
Conference: ICML 2025

Expected Variational Inequalities
Brian Hu Zhang * 1 Ioannis Anagnostides * 1 Emanuel Tewolde 1 2 Ratip Emin Berker 1 2 Gabriele Farina 3
Vincent Conitzer 1 2 4 Tuomas Sandholm 1 5 6 7
Abstract
Variational inequalities (VIs) encompass many
fundamental problems in diverse areas ranging
from engineering to economics and machine learn-
ing.
However, their considerable expressivity
comes at the cost of computational intractabil-
ity. In this paper, we introduce and analyze a
natural relaxation—which we refer to as expected
variational inequalities (EVIs)—where the goal
is to find a distribution that satisfies the VI con-
straint in expectation. By adapting recent tech-
niques from game theory, we show that, unlike
VIs, EVIs can be solved in polynomial time under
general (nonmonotone) operators. EVIs capture
the seminal notion of correlated equilibria, but
enjoy a greater reach beyond games. We also
employ our framework to capture and generalize
several existing disparate results, including from
settings such as smooth games, and games with
coupled constraints or nonconcave utilities.
1. Introduction
Variational inequalities (VIs) provide a unifying framework
for analyzing a wide range of optimization and equilib-
rium problems. They have a host of important applica-
tions in engineering and economics (Facchinei & Pang,
2003), including identifying stationary points in constrained
optimization; computing Nash equilibria in multi-player
games (Nash, 1951), such as Cournot’s classical model of
oligopoly (Cournot, 1838); predicting economic activity—
commodity prices and consumer consumption—in a closed,
competitive economy (Arrow & Debreu, 1954), which is at
the heart of general equilibrium theory; traffic equilibrium
*Equal contribution 1Carnegie Mellon University 2Foundations
of Cooperative AI Lab (FOCAL) 3Massachusetts Institute of Tech-
nology 4University of Oxford 5Strategy Robot, Inc. 6Strategic Ma-
chine, Inc. 7Optimized Markets, Inc. Correspondence to: Brian Hu
Zha...
Conference: ICML 2025

RAPID: Long-Context Inference with Retrieval-Augmented
Speculative Decoding
Guanzheng Chen * 1 2 3 Qilong Feng * 1 Jinjie Ni 1 Xin Li 2 3 Michael Qizhe Shieh 1
Code:
https://github.com/NUS-TRAIL/RAPID
Abstract
The emergence of long-context large language
models (LLMs) offers a promising alternative to
traditional retrieval-augmented generation (RAG)
for processing extensive documents. However, the
computational overhead of long-context inference
presents significant efficiency challenges. While
Speculative Decoding (SD) traditionally acceler-
ates inference using smaller draft models, its effec-
tiveness diminishes substantially in long-context
scenarios due to memory-bound KV cache op-
erations.
We introduce Retrieval-Augmented
SPeculatIve Decoding (RAPID), which leverages
RAG for both accelerating and enhancing gener-
ation quality in long-context inference. RAPID
introduces the RAG drafter—a draft LLM oper-
ating on shortened retrieval contexts—to spec-
ulate on the generation of long-context target
LLMs. Our approach enables a new paradigm
where same-scale or even larger LLMs can serve
as RAG drafters while maintaining computational
efficiency. To fully leverage the potentially supe-
rior capabilities from stronger RAG drafters, we
develop an inference-time knowledge transfer that
enriches the target distribution by RAG. Exten-
sive experiments on the LLaMA-3.1 and Qwen2.5
backbones demonstrate that RAPID effectively
integrates the strengths of both RAG and long-
context LLMs, achieving significant performance
improvements (e.g., from 39.33 to 42.83 on In-
finiteBench for LLaMA-3.1-8B) with more than
2× speedups for long-context inference. Our anal-
yses also reveal the robustness of RAPID across
various context lengths and retrieval quality.
*Equal contribution 1National University of Singapore 2DAMO
Academy, Alibaba Group 3Hupan Lab, 310023, Hangzhou, China.
Correspondence to: Guanzheng Chen <gc.chen@u.nus.edu>,
Michael Qizhe Shieh <michaelshieh@comp.nus.edu.s...
Conference: ICML 2025

Policy Gradient with Tree Expansion
Gal Dalal * 1 Assaf Hallak * 1 Gugan Thoppe 2 Shie Mannor 1 3 Gal Chechik 1 4
Abstract
Policy gradient methods are notorious for hav-
ing a large variance and high sample complexity.
To mitigate this, we introduce SoftTreeMax—a
generalization of softmax that employs planning.
In SoftTreeMax, we extend the traditional logits
with the multi-step discounted cumulative reward,
topped with the logits of future states. We an-
alyze SoftTreeMax and explain how tree expan-
sion helps to reduce its gradient variance. We
prove that the variance depends on the chosen
tree-expansion policy. Specifically, we show that
the closer the induced transitions are to being
state-independent, the stronger the variance decay.
With approximate forward models, we prove that
the resulting gradient bias diminishes with the
approximation error while retaining the same vari-
ance reduction. Ours is the first result to bound
the gradient bias for an approximate model. In
a practical implementation of SoftTreeMax, we
utilize a parallel GPU-based simulator for fast and
efficient tree expansion. Using this implementa-
tion in Atari, we show that SoftTreeMax reduces
the gradient variance by three orders of magni-
tude. This leads to better sample complexity and
improved performance compared to distributed
PPO.
1. Introduction
Policy Gradient (PG) methods (Sutton et al., 1999) for Re-
inforcement Learning (RL) are often the first choice for
environments that allow numerous interactions at a fast pace
(Schulman et al., 2017). Their success is attributed to sev-
eral factors: they are easy to distribute to multiple workers,
require no assumptions on the underlying value function,
and have both on-policy and off-policy variants.
*Equal contribution
1NVIDIA Research 2Indian Institute of
Science 3Technion University 4Bar-Ilan University. Correspon-
dence to: Gal dalal <gdalal@nvidia.com>, Assaf Hallak <ahal-
lak@nvidia.com>.
Proceedings of the 42 nd International Confer...
Conference: ICML 2025

Lightweight Protocols for Distributed Private Quantile Estimation
Anders Aamand 1 Fabrizio Boninsegna 2 Abigail Gentle 3 Jacob Imola 1 Rasmus Pagh 1
Abstract
Distributed data analysis is a large and growing
field driven by a massive proliferation of user
devices, and by privacy concerns surrounding
the centralised storage of data. We consider two
adaptive algorithms for estimating one quantile
(e.g. the median) when each user holds a sin-
gle data point lying in a domain [B] that can
be queried once through a private mechanism;
one under local differential privacy (LDP) and
another for shuffle differential privacy (shuffle-
DP). In the adaptive setting we present an ε-
LDP algorithm which can estimate any quan-
tile within error α only requiring O( log B
ε2α2 ) users,
and an (ε, δ)-shuffle DP algorithm requiring only
eO(( 1
ε2 +
1
α2 ) log B) users. Prior (nonadaptive)
algorithms require more users by several logarith-
mic factors in B. We further provide a match-
ing lower bound for adaptive protocols, showing
that our LDP algorithm is optimal in the low-ε
regime. Additionally, we establish lower bounds
against non-adaptive protocols which paired with
our understanding of the adaptive case, proves a
fundamental separation between these models.
1. Introduction
A strong trend in recent years has been towards federated
computations (Kairouz et al., 2021) in which algorithms are
run on a distributed dataset rather than by collecting data and
performing the computation in a centralized manner. This
trend is motivated by the wish to protect individuals’ data,
as well as organizations’ wish steer clear of liability issues
stemming from collecting and handling private data. The
1BARC and Department of Computer Science, Univer-
sity of Copenhagen, Denmark 2Department of Information En-
gineering, University of Padova, Italy 3School of Computer
Science, University of Sydney, Australia.
Correspondence
to:
Anders Aamand <aa@di.ku.dk>, Abigail Gentle <abi-
gail.gentle@sydney.ed...