Publications

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

Le Zhang

Bo Wang

Xipeng Qiu

Siva Reddy

Aishwarya Agrawal

We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, sign… (see more)ificantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular information retrieval benchmarks, notably requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT benchmarks. These results underscore the effectiveness of our approach and highlight how reinforcement learning can enhance LLM reasoning capabilities in reranking.

2025-05-01

arXiv (published)

Rejecting Hallucinated State Targets during Planning

Harry Zhao

Mingde Zhao

Tristan Sylvain

Romain Laroche

Doina Precup

Yoshua Bengio

2025-05-01

ICML.cc/2025/Conference (poster)

Rootlets-based registration to the spinal cord PAM50 template

Sandrine Bédard

Jan Valosek

Valeria Oliva

Kenneth A. Weber

Julien Cohen-Adad

2025-05-01

arXiv (published)

SafeArena: Evaluating the Safety of Autonomous Web Agents

Ada Defne Tur

Esin DURMUS

Karolina Stanczak

2025-05-01

ICML.cc/2025/Conference (poster)

Scaling Trends in Language Model Robustness

Nikolaus H. R. Howe

Ian R. McKenzie

Oskar John Hollinsworth

Michał Zając

Tom Tseng

Aaron David Tucker

Pierre-Luc Bacon

Adam Gleave

2025-05-01

ICML.cc/2025/Conference (poster)

SCAR: Shapley Credit Assignment for More Efficient RLHF

Meng Cao

Shuyuan Zhang

Xiaojun Chang

Doina Precup

2025-05-01

arXiv (published)

SDLog: A Deep Learning Framework for Detecting Sensitive Information in Software Logs

Roozbeh Aghili

Xingfang Wu

Foutse Khomh

Heng Li

2025-05-01

arXiv (published)

Search-Based Correction of Reasoning Chains for Language Models

Minsu Kim

Jean-Pierre R. Falet

Oliver E. Richardson

Xiaoyin Chen

Moksh J. Jain

Sungjin Ahn

Sungsoo Ahn

Yoshua Bengio

2025-05-01

arXiv (published)

Seeing the Unseen: How EMoE Unveils Bias in Text-to-Image Diffusion Models

Lucas Berry

Axel Brando

Wei-Di Chang

Juan Higuera

David Meger

2025-05-01

arXiv (published)

Self-Evolving Curriculum for LLM Reasoning

Minsu Kim

Alexandre Piché

Nicolas Gontier

Yoshua Bengio

Ehsan Kamalloo

2025-05-01

arXiv (published)

Self-Play $Q$-Learners Can Provably Collude in the Iterated Prisoner's Dilemma

Quentin Bertrand

Juan Agustin Duque

Emilio Calvano

Gauthier Gidel

A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such… (see more) as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners in the iterated prisoner’s dilemma. We characterize broad conditions under which such agents provably learn the cooperative Pavlov (win-stay, lose-shift) policy rather than the Pareto-dominated “always defect” policy. We validate our theoretical results through additional experiments, demonstrating their robustness across a broader class of deep learning algorithms.

2025-05-01

ICML.cc/2025/Conference (poster)