4 citations · 7 across the 9 of their papers we have counts for
10 papers
DiffusionGemma Technical Report
DiffusionGemma Team, Adrien Ali Taïga, James Assiene +41
We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at…
Horizon Imagination: Efficient On-Policy Rollout in Diffusion World Models
Lior Cohen, Ofir Nabati, Kaixin Wang +2
We study diffusion-based world models for reinforcement learning, which offer high generative fidelity but face critical efficiency challenges in control. Current methods either re…
Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces
Haitong Ma, Ofir Nabati, Aviv Rosenberg +7
Reinforcement learning (RL) struggles to scale to large, combinatorial action spaces common in many real-world problems. This paper introduces a novel framework for training discre…
Spectral Bellman Method: Unifying Representation and Exploration in RL
Ofir Nabati, Bo Dai, Shie Mannor +1
Representation learning is critical to the empirical and theoretical success of reinforcement learning. However, many existing methods are induced from model-learning aspects, misa…
Preference Adaptive and Sequential Text-to-Image Generation
Ofir Nabati, Guy Tennenholtz, ChihWei Hsu +5
We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user…
MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting
Chen Tessler, Yunrong Guo, Ofir Nabati +2
Crafting a single, versatile physics-based controller that can breathe life into interactive characters across a wide spectrum of scenarios represents an exciting frontier in chara…
Representation-Driven Reinforcement Learning
Ofir Nabati, Guy Tennenholtz, Shie Mannor
We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual band…
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
Ofir Nabati, Tom Zahavy, Shie Mannor
We study neural-linear bandits for solving problems where {\em both} exploration and representation learning play an important role. Neural-linear bandits harnesses the representat…
A Critical View of the Structural Causal Model
Tomer Galanti, Ofir Nabati, Lior Wolf
In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their i…
Fast and Accurate Reconstruction of Compressed Color Light Field
Ofir Nabati, David Mendlovic, Raja Giryes
Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field phot…