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Register a new userOffline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks
التعزيز غير متصل التعلم من ردود فعل إنسانية في مهام تسلسل العالم الحقيقي
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يمكن جمع كميات كبيرة من سجلات التفاعل من أنظمة NLP التي يتم نشرها في العالم الحقيقي.كيف يمكن الاستفادة من هذه الثروة من المعلومات؟يعد استخدام سجلات التفاعل هذه في إعداد تعليم التعزيز (RL) غير متصل نهجا واعدا.ومع ذلك، نظرا لطبيعة مهام NLP وقيود أنظمة الإنتاج، تنشأ سلسلة من التحديات.نقدم نظرة عامة موجزة عن هذه التحديات ومناقشة الحلول الممكنة.
Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.
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