6 papers
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…
SphericalDreamer: Generating Navigable Immersive 3D Worlds with Panorama Fusion
Antoine Schnepf, Karim Kassab, Flavian Vasile +1
The generation of immersive and navigable 3D environments is increasingly prevalent with the growing adoption of virtual reality and 3D content. However, recent methods face a fund…
RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
Imad Aouali, Flavian Vasile, Otmane Sakhi +2
LLM recommendation agents increasingly produce structured recommendation reports: sets of items accompanied by natural-language justifications. Yet existing evaluations often reduc…
Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?
Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi +5
Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice t…
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…
Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder
Antoine Schnepf, Karim Kassab, Jean-Yves Franceschi +5
While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides red…