7 papers
On Preference Coverage Collapse from Hindsight Relabeling in Multi-Objective Reinforcement Learning
Baptiste Bonin, Caro Strickland, Audrey Durand
Hindsight relabeling which retroactively replacing a transition's goal with the outcome the agent actually achieved is an effective tool for improving sample-efficiency in Reinforc…
Offline A/B Testing of Slate Recommendation Systems with LLMs: Reducing the Dependency on Pre-Collected User Interaction Data
Baptiste Bonin, Maxime Heuillet, Audrey Durand
Slate recommender systems (RecSys) present users with ordered sets of interacting items (e.g., playlists). We investigate whether large language models (LLMs) can articulate pairwi…
Robust Fine-Tuning from Non-Robust Pretrained Models: Mitigating Suboptimal Transfer With Epsilon-Scheduling
Jonas Ngnawé, Maxime Heuillet, Sabyasachi Sahoo +5
Fine-tuning pretrained models is a standard and effective workflow in modern machine learning. However, robust fine-tuning (RFT), which aims to simultaneously achieve adaptation to…
Nested-ReFT: Efficient Reinforcement Learning for Large Language Model Fine-Tuning via Off-Policy Rollouts
Maxime Heuillet, Yufei Cui, Boxing Chen +2
Advanced reasoning in LLMs on challenging domains like mathematical reasoning can be tackled using verifiable rewards based reinforced fine-tuning (ReFT). In standard ReFT framewor…
Preference-based learning for news headline recommendation
Alexandre Bouras, Audrey Durand, Richard Khoury
This study explores strategies for optimizing news headline recommendations through preference-based learning. Using real-world data of user interactions with French-language onlin…
Neural Active Learning Meets the Partial Monitoring Framework
Maxime Heuillet, Ola Ahmad, Audrey Durand
We focus on the online-based active learning (OAL) setting where an agent operates over a stream of observations and trades-off between the costly acquisition of information (label…
Randomized Confidence Bounds for Stochastic Partial Monitoring
Maxime Heuillet, Ola Ahmad, Audrey Durand
The partial monitoring (PM) framework provides a theoretical formulation of sequential learning problems with incomplete feedback. On each round, a learning agent plays an action w…