Filtered to world models · clear filter
This paper proposes a method to scale automatic research agents by replacing environment execution with a world model, which can reduce training costs and improve performance. Practitioners might care about this approach because it can accelerate training times and lead to better results for complex AI tasks.
This paper proposes a way to improve the efficiency of training world models by using game development as a source of reward signals and trajectory data, allowing for more effective post-training of large language models using reinforcement learning. Practitioners might care about this approach because it could lead to more scalable and effective world models for applications like dialogue systems and visual question answering.
This paper develops a new AI model called EVOKE that can generate interactive worlds without running out of memory or taking too long. Practitioners might care about EVOKE because it can support long-term generation and adaptation in complex environments.
This paper develops a new method for evaluating world models, called HarnessEval-W, which provides more detailed and justifiable results than existing benchmarks. Practitioners might care about HarnessEval-W because it can help them build more trustworthy world models that better align with human preferences.
This paper develops a new framework for world modeling that takes into account the mental state of agents, which is essential for predicting human decisions. Practitioners caring about human decision-making and planning might find this research useful.
This paper introduces EchoWM, an AI model that can generate immersive, interactive media, such as videos and audio, while responding to user navigation. Practitioners might care about this model for building more realistic and engaging virtual environments.