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Koa-action is a framework for low-latency atomic actions -- fast, single-step decisions such as classification, semantic endpointing, Boolean checks, and scoring -- formulated as constrained generation with single-token outputs by introducing atomic label tokens and applying supervised fine-tuning.
Obtaining the optimal action-value function in Markov decision processes is computationally intensive in large state--action spaces. In this study, we present statistically rigorous convergence results for a robust reinforcement learning algorithm warm-started by a transformer-based action-value function prediction, wh...
This paper proposes a deep learning model formally called QEmbed, built upon the Masked Autoencoder for Distribution Estimation (MADE) auto-regressive framework to learn joint data distributions for selectivity estimation, and designs a hybrid encoding scheme that combines one-hot and embedding encodings.
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