233 citations · 531 across the 34 of their papers we have counts for
7 papers · 1 filter
Off-Policy Learning to Reason Works Because It Is More Pessimistic Than You Think
Otmane Sakhi, Aleksei Arzhantsev, Imad Aouali +1
Large scale reinforcement learning has become a central tool for improving reasoning in large language models. At this scale, generation is often lagged or asynchronous, so updates…
RoiRL: Efficient, Self-Supervised Reasoning with Offline Iterative Reinforcement Learning
Aleksei Arzhantsev, Otmane Sakhi, Flavian Vasile
Reinforcement learning (RL) is central to improving reasoning in large language models (LLMs) but typically requires ground-truth rewards. Test-Time Reinforcement Learning (TTRL) r…
What Users Want? WARHOL: A Generative Model for Recommendation
Jules Samaran, Ugo Tanielian, Romain Beaumont +1
Current recommendation approaches help online merchants predict, for each visiting user, which subset of their existing products is the most relevant. However, besides being intere…
Combining Reward and Rank Signals for Slate Recommendation
Imad Aouali, Sergey Ivanov, Mike Gartrell +4
We consider the problem of slate recommendation, where the recommender system presents a user with a collection or slate composed of K recommended items at once. If the user finds…
Adversarial Training of Word2Vec for Basket Completion
Ugo Tanielian, Mike Gartrell, Flavian Vasile
In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language mo…
Rover Descent: Learning to optimize by learning to navigate on prototypical loss surfaces
Louis Faury, Flavian Vasile
Learning to optimize - the idea that we can learn from data algorithms that optimize a numerical criterion - has recently been at the heart of a growing number of research efforts.…
Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
Flavian Vasile, Damien Lefortier, Olivier Chapelle
One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed…