3.4k citations · 6.3k across the 41 of their papers we have counts for
41 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Gemma 3 Technical Report
Gemma Team, Aishwarya Kamath, Johan Ferret +209
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…
Learning from negative feedback, or positive feedback or both
Abbas Abdolmaleki, Bilal Piot, Bobak Shahriari +9
Existing preference optimization methods often assume scenarios where paired preference feedback (preferred/positive vs. dis-preferred/negative examples) is available. This require…
RRM: Robust Reward Model Training Mitigates Reward Hacking
Tianqi Liu, Wei Xiong, Jie Ren +15
Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. However, traditional RM training, which relies on response pairs tied to sp…
Building Math Agents with Multi-Turn Iterative Preference Learning
Wei Xiong, Chengshuai Shi, Jiaming Shen +10
Recent studies have shown that large language models' (LLMs) mathematical problem-solving capabilities can be enhanced by integrating external tools, such as code interpreters, and…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Multi-turn Reinforcement Learning from Preference Human Feedback
Lior Shani, Aviv Rosenberg, Asaf Cassel +10
Reinforcement Learning from Human Feedback (RLHF) has become the standard approach for aligning Large Language Models (LLMs) with human preferences, allowing LLMs to demonstrate re…
Human Alignment of Large Language Models through Online Preference Optimisation
Daniele Calandriello, Daniel Guo, Remi Munos +10
Ensuring alignment of language models' outputs with human preferences is critical to guarantee a useful, safe, and pleasant user experience. Thus, human alignment has been extensiv…
Direct Language Model Alignment from Online AI Feedback
Shangmin Guo, Biao Zhang, Tianlin Liu +9
Direct alignment from preferences (DAP) methods, such as DPO, have recently emerged as efficient alternatives to reinforcement learning from human feedback (RLHF), that do not requ…
Generalized Preference Optimization: A Unified Approach to Offline Alignment
Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng +7
Offline preference optimization allows fine-tuning large models directly from offline data, and has proved effective in recent alignment practices. We propose generalized preferenc…
Nash Learning from Human Feedback
Rémi Munos, Michal Valko, Daniele Calandriello +14
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human preferences. Typically, RLHF involves the in…
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot +4
The prevalent deployment of learning from human preferences through reinforcement learning (RLHF) relies on two important approximations: the first assumes that pairwise preference…
Unlocking the Power of Representations in Long-term Novelty-based Exploration
Alaa Saade, Steven Kapturowski, Daniele Calandriello +6
We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for…
The Edge of Orthogonality: A Simple View of What Makes BYOL Tick
Pierre H. Richemond, Allison Tam, Yunhao Tang +3
Self-predictive unsupervised learning methods such as BYOL or SimSiam have shown impressive results, and counter-intuitively, do not collapse to trivial representations. In this wo…
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
BYOL-Explore: Exploration by Bootstrapped Prediction
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar +11
We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, t…
Shaking the foundations: delusions in sequence models for interaction and control
Pedro A. Ortega, Markus Kunesch, Grégoire Delétang +16
The recent phenomenal success of language models has reinvigorated machine learning research, and large sequence models such as transformers are being applied to a variety of domai…
Geometric Entropic Exploration
Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Alaa Saade +7
Exploration is essential for solving complex Reinforcement Learning (RL) tasks. Maximum State-Visitation Entropy (MSVE) formulates the exploration problem as a well-defined policy…
BYOL works even without batch statistics
Pierre H. Richemond, Jean-Bastien Grill, Florent Altché +8
Bootstrap Your Own Latent (BYOL) is a self-supervised learning approach for image representation. From an augmented view of an image, BYOL trains an online network to predict a tar…
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill, Florian Strub, Florent Altché +11
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target…
Acme: A Research Framework for Distributed Reinforcement Learning
Matthew W. Hoffman, Bobak Shahriari, John Aslanides +36
Deep reinforcement learning (RL) has led to many recent and groundbreaking advances. However, these advances have often come at the cost of both increased scale in the underlying a…