CAPSULE learns probabilistic control-affine dynamics offline to construct uncertainty-incorporating control barrier functions that enforce conservative safety constraints via online action correction in reinforcement learning.
InInternational conference on machine learning
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Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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CAPSULE: Control-Theoretic Action Perturbations for Safe Uncertainty-Aware Reinforcement Learning
CAPSULE learns probabilistic control-affine dynamics offline to construct uncertainty-incorporating control barrier functions that enforce conservative safety constraints via online action correction in reinforcement learning.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.