AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Alignment-Guided Flow Transformer (AGFT) is presented, a novel framework that explicitly enforces tri-modal alignment through a dedicated alignment loss, bridging the representational gap across modalities and enhancing task adaptation.
This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline dataset. This problem is inherently challenging as it requires balancing three highly interconnected and competing objectives: satisfying safety constraints, maximizin...
Continual offline reinforcement learning (CORL) has shown impressive ability in diffusion-based continual learning systems by modeling the joint distributions of trajectories. However, most research only focuses on limited continual task settings where the tasks have the same observation and action space, which deviate...
ExToken is introduced, a simple yet general framework that condition VLA policies on discrete behavioral priors derived from offline demonstrations for structured exploration that consistently accelerates convergence, improves task performance, and exhibits strong robustness under highly constrained interaction budgets...
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