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Generative Flow Networks (GFlowNets) learn policies that sample discrete objects in proportion to a positive reward. They are trained by minimizing balance objectives, whose exact satisfaction guarantees correct sampling. However, a comprehensive theory linking nonzero balance loss and approximate stationarity to sampl...
The proposed MarginFlow framework is a framework that turns the design of the proposal into a learning problem and amortizes it across margins by exploiting their self-similarity.
Loading reusable skill documents into a bounded context window has become a primary way large language model (LLM) agents acquire task-specific capabilities, which makes skill selection a first-order determinant of task performance and token cost. Yet current agents score skills independently by semantic relevance and...
FlexLoop is proposed, a novel post-training framework that converts pretrained fixed-depth looped policies into depth-elastic policies that supports reliable inference across recurrent depths and enables state-wise adaptive inference through recurrent-depth consistency.
AV-AIVAT turns variance reduction into efficient, auditable early stopping while separating asymptotic screening from exact certification, so an evaluation can stop the moment its evidence suffices and hand a third party everything needed to recheck the verdict at that very stopping time.
The authors' bounds match existing delay-free regret guarantees for Lipschitz bandits and characterize the additional $\tilde{O}(\sqrt{D})$ impact introduced by feedback delays.
It is shown that the frozen agent LLM already carries the routing signal in its own forward passes, and that two linear maps suffice to read it out with no skill text in the context, and Gavel (Glance And Verdict from a frozen LLM) reads it in two steps.
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