REINFORCEMENT LEARNING
CORE MACHINE LEARNING
TaskMet: Task-driven Metric Learning for Model Learning
December 11, 2023
Abstract
Deep learning models are often used with some downstream task. Models solely trained to achieve accurate predictions may struggle to perform well on the desired downstream tasks. We propose using the task loss to learn a metric which parameterizes a loss to train the model. This approach does not alter the optimal prediction model itself, but rather changes the model learning to emphasize the information important for the downstream task. This enables us to achieve the best of both worlds: a prediction model trained in the original prediction space while also being valuable for the desired downstream task. We validate our approach through experiments conducted in two main settings: 1) decision-focused model learning scenarios involving portfolio optimization and budget allocation, and 2) reinforcement learning in noisy environments with distracting states.
AUTHORS
Publisher
NeurIPS
Related Publications
May 12, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
December 26, 2025
REINFORCEMENT LEARNING
NLP
Safety Alignment of LMs via Non-cooperative Games
Brandon Amos, Anselm Paulus, Arman Zharmagambetov, Ilia Kulikov, Ivan Evtimov, Kamalika Chaudhuri, Remi Munos
December 26, 2025
December 01, 2025
CONVERSATIONAL AI
REINFORCEMENT LEARNING
Rubric-Based Benchmarking and Reinforcement Learning for Advancing LLM Instruction Following
Amine Benhalloum, Hany Awadalla, Hejia Zhang, Hunter Lang, Julian Katz-Samuels, Karishma Mandyam, Licheng Yu, Manaal Faruqui, Maryam Fazel-Zarandi, Nanshu Wang, Qi Qi, Richard Yuanzhe Pang, Selina Xiaoliang Peng, Shengjie Bi, Shengyu Feng, Shishir G. Patil, Sopan Khosla, Sujan Gonugondla, Vincent Li, Wenzhe Li, Yuanhao Xiong, Yue Yu, Yun He, Yundi Qian
December 01, 2025
November 18, 2025
RESEARCH
CORE MACHINE LEARNING
Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance
Roberta Raileanu, * Equal authorship, Alexis Audran-Reiss, Amar Budhiraja *, Anton Protopopov, Bhavul Gauri, Despoina Magka, Gaurav Chaurasia, Michael Slater, Shalini Maiti *, Tatiana Shavrina, Yoram Bachrach
November 18, 2025
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026