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Reward learning enables the application of reinforcement learning (RL) to tasks where reward is defined by human judgment, building a model of reward by asking humans questions.
Preference-based interactive multi-document summarisation
Yang Gao, Christian M Meyer, and Iryna Gurevych · 1906
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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