Chatbots That Learn From People — Even When They Can’t Practice Live
Ever wonder if a chatbot could get better just by looking at old conversations? We made a way for models to learn from a fixed set of real human chats, without needing to explore live.
Starting from a model trained on lots of text, the system gently keeps new answers close to what it already knows so it won't go wild.
It also uses a simple trick to notice when it's unsure and be cautious with those guesses.
That means the same records can teach many different ideas of what makes a good reply, even after the data was already collected.
We tested this with open conversations that have huge choice spaces, and then put the system live to talk with people.
The result was replies that feel more natural and better than older ones, learned while avoiding risky online trying.
This could help build chat systems that learn from human data while staying offline, safe, reliable and able to match different preferences you might have.
Read article comprehensive review in Paperium.net:
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferencesin Dialog
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