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
Diganta Misra
Shreeya Dhakal
Jonathan Rystrøm
Roman Solomatin
Ömer Veysel Çağatan
Akash Kundu … (voir 62 de plus)
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
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
Mariya Hendriksen
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
Lasse Hansen
Sara Hooker
Chenghao Xiao
Vaibhav Adlakha
Orion Weller
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… (voir plus) 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.
MODL: Multilearner Online Deep Learning
Antonios Valkanas
Boris Oreshkin
Online deep learning solves the problem of learning from streams of data, reconciling two opposing objectives: learn fast and learn deep. Ex… (voir plus)isting work focuses almost exclusively on exploring pure deep learning solutions, which are much better suited to handle the"deep"than the"fast"part of the online learning equation. In our work, we propose a different paradigm, based on a hybrid multilearner approach. First, we develop a fast online logistic regression learner. This learner does not rely on backpropagation. Instead, it uses closed form recursive updates of model parameters, handling the fast learning part of the online learning problem. We then analyze the existing online deep learning theory and show that the widespread ODL approach, currently operating at complexity
Multi-agent cooperation through learning-aware policy gradients
Alexander Meulemans
Seijin Kobayashi
Johannes Von Oswald
Nino Scherrer
Eric Elmoznino
Blaise Aguera y Arcas
João Sacramento
Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. How can we achieve cooperation… (voir plus) among self-interested, independent learning agents? Promising recent work has shown that in certain tasks cooperation can be established between learning-aware agents who model the learning dynamics of each other. Here, we present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning, which takes into account that other agents are themselves learning through trial and error based on multiple noisy trials. We then leverage efficient sequence models to condition behavior on long observation histories that contain traces of the learning dynamics of other agents. Training long-context policies with our algorithm leads to cooperative behavior and high returns on standard social dilemmas, including a challenging environment where temporally-extended action coordination is required. Finally, we derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
Multi-session, multi-task neural decoding from distinct cell-types and brain regions
Mehdi Azabou
Krystal Xuejing Pan
Vinam Arora
Ian Jarratt Knight
Eva L Dyer
Recent work has shown that scale is important for improved brain decoding, with more data leading to greater decoding accuracy. However, lar… (voir plus)ge-scale decoding across many different datasets is challenging because neural circuits are heterogeneous---each brain region contains a unique mix of cellular sub-types, and the responses to different stimuli are diverse across regions and sub-types. It is unknown whether it is possible to pre-train and transfer brain decoding models between distinct tasks, cellular sub-types, and brain regions. To address these questions, we developed a multi-task transformer architecture and trained it on the entirety of the Allen Institute's Brain Observatory dataset. This dataset contains responses from over 100,000 neurons in 6 areas of the brains of mice, observed with two-photon calcium imaging, recorded while the mice observed different types of visual stimuli. Our results demonstrate that transfer is indeed possible -combining data from different sources is beneficial for a number of downstream decoding tasks. As well, we can transfer the model between regions and sub-types, demonstrating that there is in fact common information in diverse circuits that can be extracted by an appropriately designed model. Interestingly, we found that the model's latent representations showed clear distinctions between different brain regions and cellular sub-types, even though it was never given any information about these distinctions. Altogether, our work demonstrates that training a large-scale neural decoding model on diverse data is possible, and this provides a means of studying the differences and similarities between heterogeneous neural circuits.
Neuroplastic Expansion in Deep Reinforcement Learning
Jiashun Liu
Johan Samir Obando Ceron
Ling Pan
Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching
Arnav Kumar Jain
Harley Wiltzer
Jesse Farebrother
Sanjiban Choudhury
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Avila Pires
Mark Rowland
Diana Borsa
Zhaohan Daniel Guo
Andre Barreto
David Abel
Remi Munos
Will Dabney
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standar… (voir plus)d reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.
Optimizing Return Distributions with Distributional Dynamic Programming
Bernardo Avila Pires
Mark Rowland
Diana Borsa
Zhaohan Daniel Guo
Andre Barreto
David Abel
Remi Munos
Will Dabney
We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standar… (voir plus)d reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.
OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning
Xiaoqiang Wang
Large language models (LLMs) and large multimodal models (LMMs) have shown great potential in automating complex tasks like web browsing and… (voir plus) gaming. However, their ability to generalize across diverse applications remains limited, hindering broader utility. To address this challenge, we present OSCAR: Operating System Control via state-Aware reasoning and Re-planning. OSCAR is a generalist agent designed to autonomously navigate and interact with various desktop and mobile applications through standardized controls, such as mouse and keyboard inputs, while processing screen images to fulfill user commands. OSCAR translates human instructions into executable Python code, enabling precise control over graphical user interfaces (GUIs). To enhance stability and adaptability, OSCAR operates as a state machine, equipped with error-handling mechanisms and dynamic task re-planning, allowing it to efficiently adjust to real-time feedback and exceptions. We demonstrate OSCAR’s effectiveness through extensive experiments on diverse benchmarks across desktop and mobile platforms, where it transforms complex workflows into simple natural language commands, significantly boosting user productivity. Our code will be open-source upon publication.
ParetoFlow: Guided Flows in Multi-Objective Optimization
Ye Yuan
Can Chen
In offline multi-objective optimization (MOO), we leverage an offline dataset of designs and their associated labels to simultaneously minim… (voir plus)ize multiple objectives. This setting more closely mirrors complex real-world problems compared to single-objective optimization. Recent works mainly employ evolutionary algorithms and Bayesian optimization, with limited attention given to the generative modeling capabilities inherent in such data. In this study, we explore generative modeling in offline MOO through flow matching, noted for its effectiveness and efficiency. We introduce ParetoFlow, specifically designed to guide flow sampling to approximate the Pareto front. Traditional predictor (classifier) guidance is inadequate for this purpose because it models only a single objective. In response, we propose a multi-objective predictor guidance module that assigns each sample a weight vector, representing a weighted distribution across multiple objective predictions. A local filtering scheme is introduced to address non-convex Pareto fronts. These weights uniformly cover the entire objective space, effectively directing sample generation towards the Pareto front. Since distributions with similar weights tend to generate similar samples, we introduce a neighboring evolution module to foster knowledge sharing among neighboring distributions. This module generates offspring from these distributions, and selects the most promising one for the next iteration. Our method achieves state-of-the-art performance across various tasks.
Performative Prediction on Games and Mechanism Design
António Góis
Mehrnaz Mofakhami
Fernando P. Santos
PETRA: Parallel End-to-end Training with Reversible Architectures
Stephane Rivaud
Louis Fournier
Thomas Pumir
Michael Eickenberg
Edouard Oyallon
Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep l… (voir plus)earning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on CIFAR-10, ImageNet32, and ImageNet, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models.