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A unified view is developed in which the log-probability ratio between a proxy expert and a proxy reference defines an implicit reward for KL-regularized policy optimization, which enables any-to-any transfer across models that differ in scale, training stage or generation.
This work proposes Counterfactual Evidence Disentanglement (CED), a training-time evidence audit for VLM grounding, which outperforms prior RL-based post-training methods, with targeted analyses verifying its object-centric signal.
Proxy OPD is introduced, an asynchronous post-training framework that transfers reward-induced policy improvements rather than absolute policy distributions and establishes relative policy updates as highly reusable, adjustable assets for scalable, reward-based post-training.
This paper presents IterCAD, a unified multimodal agent framework for closed-loop, interactive CAD generation and editing, and introduces the IterCAD-Bench evaluation suite, establishing a survivor-bias-free standard that unifies code validity and geometric precision.
This work conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile f...
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