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DTSTART:20261101T010000
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DESCRIPTION:World models offer a compelling framework for training agents by learning a simulation of the environment\, a paradigm quickly gaining traction for its potential to solve data scarcity in fields like robotics. However\, a fundamental open question remains: Is it truly feasible to build world models that can accurately simulate complex environments for agent training?\nIn this talk\, I will introduce Jasmine\, a performant open-source JAX-based world modeling codebase\, representing our first milestone towards answering this question. Jasmine is a production-ready framework built for scalability\, enabling researchers to train diverse world model architectures—from Transformers to diffusion models—across hundreds of accelerators. I will discuss the engineering decisions behind Jasmine that make it a robust tool for large-scale research and how we are using it to establish "Empirical Environment Complexity Scaling Laws” by systematically measuring the compute and data requirements to model environments of increasing complexity\, providing a concrete path towards understanding the future of world models and their role in building more capable agents.\n\nFranz is co-founder of pdoom.org\, an open research community focused on addressing core blockers towards general intelligence that will not be solved by scaling up compute. His work spans the entire stack from kernel-level optimization to large-scale distributed systems. His previous work includes open ML infrastructure\, crowd-sourced software engineering datasets as well as pre-training and reinforcement learning of models at scale. He previously worked at Celonis\, is founding member of neuroTUM\, and has contributed to the Linux kernel.\n\n------\n\nCreate your own Add to Calendar links with addevent.com/r/a \n
X-ALT-DESC;FMTTYPE=text/html:World models offer a compelling framework for training agents by learning a simulation of the environment, a paradigm quickly gaining traction for its potential to solve data scarcity in fields like robotics. However, a fundamental open question remains: Is it truly feasible to build world models that can accurately simulate complex environments for agent training?<br />In this talk, I will introduce Jasmine, a performant open-source JAX-based world modeling codebase, representing our first milestone towards answering this question. Jasmine is a production-ready framework built for scalability, enabling researchers to train diverse world model architectures—from Transformers to diffusion models—across hundreds of accelerators. I will discuss the engineering decisions behind Jasmine that make it a robust tool for large-scale research and how we are using it to establish "Empirical Environment Complexity Scaling Laws” by systematically measuring the compute and data requirements to model environments of increasing complexity, providing a concrete path towards understanding the future of world models and their role in building more capable agents.<br><br>Franz is co-founder of pdoom.org, an open research community focused on addressing core blockers towards general intelligence that will not be solved by scaling up compute. His work spans the entire stack from kernel-level optimization to large-scale distributed systems. His previous work includes open ML infrastructure, crowd-sourced software engineering datasets as well as pre-training and reinforcement learning of models at scale. He previously worked at Celonis, is founding member of neuroTUM, and has contributed to the Linux kernel.<br /><br />------<br /><br />Create your own Add to Calendar links with addevent.com/r/a <br>
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SUMMARY:Franz Srambical - Jasmine: A Simple\, Performant and Scalable JAX-based World Modeling Codebase (Eff)
DTSTART;TZID=America/Los_Angeles:20250910T090000
DTEND;TZID=America/Los_Angeles:20250910T100000
DTSTAMP:20260902T051331Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:0
LOCATION:https://meet.google.com/wdk-yipf-zjd?hs=122&authuser=0
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