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This paper introduces UniEvo-VL, a self-improvement framework for multimodal models that uses their own feedback to learn and improve, without relying on an external teacher. Practitioners might care because it can lead to better image generation capabilities and more efficient models.
This paper introduces PhiZero, a world model that uses physical language to predict how the physical world evolves, allowing for more explicit reasoning and potentially more realistic simulations. Practitioners might care because this approach could lead to more realistic and interactive world modeling in applications like robotics, video games, and virtual reality.
This paper proposes a new method called Self-Supervised Visual On-Policy Distillation (S^2VOPD) that generates learning signals from asymmetric augmented views of images, allowing for effective on-policy learning without privileged information. Practitioners might care about this paper because it presents a simple yet effective way to improve performance on various perception benchmarks.
This paper investigates whether on-policy distillation (OPD) truly improves student policies by analyzing the effects of noisy teacher supervision. It finds that OPD works by suppressing low-probability tokens, which can be achieved without a teacher, and introduces a new method called On-Policy Self-Adaptation (OPSA) that outperforms OPD and traditional reinforcement learning methods.