4 papers
Simulus: Combining Improvements in Sample-Efficient World Model Agents
Lior Cohen, Kaixin Wang, Bingyi Kang +2
World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements unrealized due to the significant expe…
Policy Optimized Text-to-Image Pipeline Design
Uri Gadot, Rinon Gal, Yftah Ziser +2
Text-to-image generation has evolved beyond single monolithic models to complex multi-component pipelines. These combine fine-tuned generators, adapters, upscaling blocks and even…
Policy Gradient with Tree Search: Avoiding Local Optimas through Lookahead
Uri Koren, Navdeep Kumar, Uri Gadot +3
Classical policy gradient (PG) methods in reinforcement learning frequently converge to suboptimal local optima, a challenge exacerbated in large or complex environments. This work…
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression
Uri Gadot, Assaf Shocher, Shie Mannor +2
Video encoders optimize compression for human perception by minimizing reconstruction error under bit-rate constraints. In many modern applications such as autonomous driving, an o…