Input Space Mode Connectivity in Deep Neural Networks
Jakub Vrabel
Ori Shem-Ur
Yaron Oz
David Scott Krueger
We extend the concept of loss landscape mode connectivity to the input space of deep neural networks. Initially studied in parameter space, … (see more)mode connectivity describes the existence of low-loss paths between solutions (loss minimizers) found via gradient descent. We present theoretical and empirical evidence of its presence in the input space of deep networks, thereby highlighting the broader nature of the phenomenon. We observe that different input images with similar predictions are generally connected, and for trained models, the path tends to be simple, with only a small deviation from being a linear path. We conjecture that input space mode connectivity in high-dimensional spaces is a geometric phenomenon, present even in untrained models, and can be explained by percolation theory. We exploit mode connectivity to obtain new insights about adversarial examples and show its potential for adversarial detection and interpretability.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu
Abhay Puri
Juan A. Rodriguez
Amirhossein Abaskohi
Alexandre Drouin
Mohammad Chegini
Perouz Taslakian
Valentina Zantedeschi
Alexandre Lacoste
David Vazquez
Nicolas Chapados
Chris Pal
Sai Rajeswar
Issam Hadj Laradji
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Interpreting Emergent Planning in Model-Free Reinforcement Learning
Thomas Bush
Stephen Chung
Usman Anwar
Adrià Garriga-Alonso
David Scott Krueger
We present the first mechanistic evidence that model-free reinforcement learning agents can learn to plan. This is achieved by applying a me… (see more)thodology based on concept-based interpretability to a model-free agent in Sokoban -- a commonly used benchmark for studying planning. Specifically, we demonstrate that DRC, a generic model-free agent introduced by [Guez et al. (2019)](https://arxiv.org/abs/1901.03559), uses learned concept representations to internally formulate plans that both predict the long-term effects of actions on the environment and influence action selection. Our methodology involves: (1) probing for planning-relevant concepts, (2) investigating plan formation within the agent's representations, and (3) verifying that discovered plans (in the agent's representations) have a causal effect on the agent's behavior through interventions. We also show that the emergence of these plans coincides with the emergence of a planning-like property: the ability to benefit from additional test-time compute. Finally, we perform a qualitative analysis of the planning algorithm learned by the agent and discover a strong resemblance to parallelized bidirectional search. Our findings advance understanding of the internal mechanisms underlying planning behavior in agents, which is important given the recent trend of emergent planning and reasoning capabilities in LLMs through RL.
2025-01-22
ICLR.cc/2025/Conference (oral)
openreview.net
openreview.net
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning
Haque Ishfaq
Guangyuan Wang
Mohammad Sami Nur Islam
Doina Precup
Existing actor-critic algorithms, which are popular for continuous control reinforcement learning (RL) tasks, suffer from poor sample effici… (see more)ency due to lack of principled exploration mechanism within them. Motivated by the success of Thompson sampling for efficient exploration in RL, we propose a novel model-free RL algorithm, Langevin Soft Actor Critic (LSAC), which prioritizes enhancing critic learning through uncertainty estimation over policy optimization. LSAC employs three key innovations: approximate Thompson sampling through distributional Langevin Monte Carlo (LMC) based
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Seanie Lee
Minsu Kim
Lynn Cherif
David Dobre
Juho Lee
Sung Ju Hwang
Kenji Kawaguchi
Gauthier Gidel
Yoshua Bengio
Nikolay Malkin
Moksh J. Jain
Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of lar… (see more)ge language models (LLMs). Developing effective protection against many modes of attack prompts requires discovering diverse attacks. Automated red-teaming typically uses reinforcement learning to fine-tune an attacker language model to generate prompts that elicit undesirable responses from a target LLM, as measured, for example, by an auxiliary toxicity classifier. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. As a flexible and probabilistically principled alternative, we propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts. We find that the attacks generated by our method are effective against a wide range of target LLMs, both with and without safety tuning, and transfer well between target LLMs. Finally, we demonstrate that models safety-tuned using a dataset of red-teaming prompts generated by our method are robust to attacks from other RL-based red-teaming approaches.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
Learning Versatile Optimizers on a Compute Diet
Abhinav Moudgil
Boris Knyazev
Guillaume Lajoie
Eugene Belilovsky
Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned upda… (see more)te rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after meta-training, is strong meta-generalization: the ability to apply the optimizers to new tasks. Recent state-of-the-art work in learned optimizers, VeLO (Metz et al., 2022), requires a large number of highly diverse meta-training tasks along with massive computational resources, 4000 TPU months, to achieve meta-generalization. This makes further improvements to such learned optimizers impractical. In this work, we identify several key elements in learned optimizer architectures and meta-training procedures that can lead to strong meta-generalization. We also propose evaluation metrics to reliably assess quantitative performance of an optimizer at scale on a set of evaluation tasks. Our proposed approach, Celo, makes a significant leap in improving the meta-generalization performance of learned optimizers and also outperforms tuned state-of-the-art optimizers on a diverse set of out-of-distribution tasks, despite being meta-trained for just 24 GPU hours.
2025-01-22
ArXiv (preprint)
doi.org
arxiv.org
Learning Versatile Optimizers on a Compute Diet
Abhinav Moudgil
Boris Knyazev
Guillaume Lajoie
Eugene Belilovsky
Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned upda… (see more)te rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after meta-training, is strong meta-generalization: the ability to apply the optimizers to new tasks. Recent state-of-the-art work in learned optimizers, VeLO (Metz et al., 2022), requires a large number of highly diverse meta-training tasks along with massive computational resources, 4000 TPU months, to achieve meta-generalization. This makes further improvements to such learned optimizers impractical. In this work, we identify several key elements in learned optimizer architectures and meta-training procedures that can lead to strong meta-generalization. We also propose evaluation metrics to reliably assess quantitative performance of an optimizer at scale on a set of evaluation tasks. Our proposed approach, Celo, makes a significant leap in improving the meta-generalization performance of learned optimizers and also outperforms tuned state-of-the-art optimizers on a diverse set of out-of-distribution tasks, despite being meta-trained for just 24 GPU hours.
2025-01-22
ArXiv (preprint)
arxiv.org
arxiv.org
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Martin Klissarov
Mikael Henaff
Roberta Raileanu
Shagun Sodhani
Pascal Vincent
Amy Zhang
Pierre-Luc Bacon
Doina Precup
Marlos C. Machado
Pierluca D'Oro
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an… (see more) AI system. We present MaestroMotif, a method for AI-assisted skill design, which yields high-performing and adaptable agents. MaestroMotif leverages the capabilities of Large Language Models (LLMs) to effectively create and reuse skills. It first uses an LLM's feedback to automatically design rewards corresponding to each skill, starting from their natural language description. Then, it employs an LLM's code generation abilities, together with reinforcement learning, for training the skills and combining them to implement complex behaviors specified in language. We evaluate MaestroMotif using a suite of complex tasks in the NetHack Learning Environment (NLE), demonstrating that it surpasses existing approaches in both performance and usability.
2025-01-22
ICLR.cc/2025/Conference (oral)
doi.org
openreview.net
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li
Tianyu Zhang
Zhiqi Bu
Suyuchen Wang
Huan He
Jie Fu
Yonghui Wu
Jiang Bian
Yong Chen
Yoshua Bengio
Model merging has emerged as an effective approach to combine multiple single-task models, fine-tuned from the same pre-trained model, into … (see more)a multitask model. This process typically involves computing a weighted average of the model parameters without any additional training. Existing model-merging methods focus on enhancing average task accuracy. However, interference and conflicts between the objectives of different tasks can lead to trade-offs during model merging. In real-world applications, a set of solutions with various trade-offs can be more informative, helping practitioners make decisions based on diverse preferences. In this paper, we introduce a novel low-compute algorithm, Model Merging with Amortized Pareto Front (MAP). MAP identifies a Pareto set of scaling coefficients for merging multiple models to reflect the trade-offs. The core component of MAP is approximating the evaluation metrics of the various tasks using a quadratic approximation surrogate model derived from a pre-selected set of scaling coefficients, enabling amortized inference. Experimental results on vision and natural language processing tasks show that MAP can accurately identify the Pareto front. To further reduce the required computation of MAP, we propose (1) a Bayesian adaptive sampling algorithm and (2) a nested merging scheme with multiple stages.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
MatExpert: Decomposing Materials Discovery By Mimicking Human Experts
Qianggang Ding
Santiago Miret
Bang Liu
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic
Xi Zhang
Brandon Amos
Mathieu Blanchette
Leo J Lee
Yoshua Bengio
Alexander Tong
Kirill Neklyudov
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynam… (see more)ics of communicating cells or physical particles. Learning the dynamics of such systems is essential for predicting the temporal evolution of populations across novel samples and unseen environments. Flow-based models allow for learning these dynamics at the population level - they model the evolution of the entire distribution of samples. However, current flow-based models are limited to a single initial population and a set of predefined conditions which describe different dynamics. We argue that multiple processes in natural sciences have to be represented as vector fields on the Wasserstein manifold of probability densities. That is, the change of the population at any moment in time depends on the population itself due to the interactions between samples. In particular, this is crucial for personalized medicine where the development of diseases and their respective treatment response depends on the microenvironment of cells specific to each patient. We propose Meta Flow Matching (MFM), a practical approach to integrating along these vector fields on the Wasserstein manifold by amortizing the flow model over the initial populations. Namely, we embed the population of samples using a Graph Neural Network (GNN) and use these embeddings to train a Flow Matching model. This gives MFM the ability to generalize over the initial distributions unlike previously proposed methods. We demonstrate the ability of MFM to improve prediction of individual treatment responses on a large scale multi-patient single-cell drug screen dataset.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
MMTEB: Massive Multilingual Text Embedding Benchmark
Kenneth Enevoldsen
Isaac Chung
Imene Kerboua
Márton Kardos
Ashwin Mathur
David Stap
Jay Gala
Wissam Siblini
Dominik Krzemiński
Genta Indra Winata
Saba Sturua
Saiteja Utpala
Mathieu Ciancone
Marion Schaeffer
Gabriel Sequeira
Diganta Misra
Shreeya Dhakal
Jonathan Rystrøm
Roman Solomatin
Ömer Veysel Çağatan … (see 66 more)
Akash Kundu
Martin Bernstorff
Shitao Xiao
Akshita Sukhlecha
Bhavish Pahwa
Rafał Poświata
Kranthi Kiran GV
Shawon Ashraf
Daniel Auras
Björn Plüster
Jan Philipp Harries
Loïc Magne
Isabelle Mohr
Mariya Hendriksen
Dawei Zhu
Hippolyte Gisserot-Boukhlef
Tom Aarsen
Jan Kostkan
Konrad Wojtasik
Taemin Lee
Marek Suppa
Crystina Zhang
Roberta Rocca
Mohammed Hamdy
Andrianos Michail
John Yang
Manuel Faysse
Aleksei Vatolin
Nandan Thakur
Manan Dey
Dipam Vasani
Pranjal A Chitale
Simone Tedeschi
Nguyen Tai
Artem Snegirev
Michael Günther
Mengzhou Xia
Weijia Shi
Xing Han Lu
Jordan Clive
Gayatri K
Maksimova Anna
Silvan Wehrli
Maria Tikhonova
Henil Shalin Panchal
Aleksandr Abramov
Malte Ostendorff
Zheng Liu
Simon Clematide
Lester James Validad Miranda
Alena Fenogenova
Guangyu Song
Ruqiya Bin Safi
Wen-Ding Li
Alessia Borghini
Federico Cassano
Hongjin Su
Jimmy Lin
Howard Yen
Lasse Hansen
Sara Hooker
Chenghao Xiao
Vaibhav Adlakha
Orion Weller
Siva Reddy
Niklas Muennighoff
Text embeddings are typically evaluated on a narrow set of tasks, limited in terms of languages, domains, and task types. To circumvent this… (see more) limitation and to provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) -- a large-scale community-driven initiative expanding MTEB to over 500 quality-controlled evaluation tasks across 1,000+ languages. MMTEB includes a wide range of challenging novel tasks such as instruction following, long-document retrieval, and code retrieval, and represents the largest multilingual collection of evaluation tasks for embedding models to date. We use this collection to construct multiple highly multilingual benchmarks. We evaluate a representative set of models on these benchmarks. Our findings indicate that, while LLM-based models can achieve state-of-the-art performance on a subset of languages, the best-performing publicly available model across languages is the notably smaller, multilingual-e5-large-instruct. Massive benchmarks often impose high computational demands, limiting accessibility, particularly for low-resource communities. To address this, we downsample tasks based on inter-task correlation (i.e., selecting only a diverse set of tasks) while preserving relative rankings. We further optimize tasks such as retrieval by sampling hard negatives, creating smaller but effective splits. These optimizations allow us to introduce benchmarks at a significantly lower computational cost. For instance, we introduce a new zero-shot English benchmark that maintains a similar ordering at a fraction of the cost.
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net