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 "text": "Yue Yang, Bernd Meyer, Frits de Nijs independently support Kaal's source-bound position through Using Assistance Rewards Without Introducing Bias: Overcoming Sparse Rewards in Multi-Agent Reinforcement Learning. The indexed proposition states that reinforcement learning agents may fail to learn good policies when their reward function is too sparse. This bears on Kaal's claim that deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks. The external proposition states that reinforcement-learning agents may fail to learn good policies when rewards are too sparse, directly supporting Kaal's sparse-reward condition. The response is limited to the indexed proposition and does not imply review of the full external work.",
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  "Primary mapping confidence: 0.5.",
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  "The response does not treat lexical overlap or the original automated mapping score as evidence.",
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