Recent vision-language-action (VLA) policies demonstrate promising generalization across diverse short-horizon tasks. However, they remain unreliable on long-horizon tasks, partly because the large-scale training data is biased toward single-stage manipulation tasks that are cheaper to demonstrate. A single weak atomic...
Zi-Wen Li, Hanlue Zhang, Zhen-Yang Ren et al.· 0 citations
Multi-instance partial-label learning (MIPL) addresses inexact supervision in both the instance and label spaces, which can be applied to video classification. However, bag-level labels do not explicitly supervise the correspondence between candidate classes and temporal evidence. We propose {\ours}, which couples labe...
Ling-Yu Shen, Wei Tang, Fakhri Karray et al.· 0 citations
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formulation can only report how often the model policy succeeds, not which solution actually worked, and because probabilities sum to one, reinforc...
A. Asadulaev, Aladin Djuhera, Karim Salta et al.· 0 citations
This work introduces a novel, scalable branched flow-matching algorithm designed to solve the branched optimal transport problem in high dimensions and demonstrates its effectiveness on challenging high-dimensional tasks in biology and image generation.
S. Semenov, Viktor Kovalchuk, Meir Roketlishvili et al.· 0 citations
A phenomenon in which agent heterogeneity gradually decreases during simulation is revealed, and Prospect-State Propagation for Multi-Agent Systems (PspMAS) is proposed, Inspired by prospect theory, which decouples each agent's micro state into a compact Prospect State and an expressive Semantic State.
A capacity-headroom hypothesis is proposed, which states that PPO performance at the SLM scale depends on both a fluent supervised model and a discriminative reward signal, rather than on the number of model parameters.
