8 papers
Generative Models in Decision Making: A Survey
Xinyu Shao, Jianping Zhang, Haozhi Wang +9
Generative models have fundamentally reshaped the landscape of decision-making, reframing the problem from pure scalar reward maximization to high-fidelity trajectory generation an…
JPmHC Dynamical Isometry via Orthogonal Hyper-Connections
Biswa Sengupta, Jinhua Wang, Leo Brunswic
Recent advances in deep learning, exemplified by Hyper-Connections (HC), have expanded the residual connection paradigm by introducing wider residual streams and diverse connectivi…
CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance
Rui Heng Yang, Xuan Zhao, Leo Maxime Brunswic +5
In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal perf…
Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising
Mateo Clemente, Leo Brunswic, Rui Heng Yang +5
Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originall…
Omni-Thinker: Scaling Multi-Task RL in LLMs with Hybrid Reward and Task Scheduling
Derek Li, Jiaming Zhou, Leo Maxime Brunswic +8
The pursuit of general-purpose artificial intelligence depends on large language models (LLMs) that can handle both structured reasoning and open-ended generation. We present Omni-…
A Theory of Multi-Agent Generative Flow Networks
Leo Maxime Brunswic, Haozhi Wang, Shuang Luo +3
Generative flow networks utilize a flow-matching loss to learn a stochastic policy for generating objects from a sequence of actions, such that the probability of generating a patt…