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Agents trained with T2RD can generalize learned policies across environments without overfitting to irrelevant features, achieving state-of-the-art performance in VRL tasks.
Local motion representations can drastically improve reinforcement learning efficiency and transferability across diverse tasks, challenging the conventional global modeling approach.
Temporal correlations in video data can unlock a new level of sample efficiency and performance in Reinforcement Learning pre-training.
Current language agents are still far from matching human expert performance when faced with real-world professional tasks requiring complex reasoning, authoritative source retrieval, and domain-specific knowledge, as revealed by the new \$OneMillion-Bench benchmark.
