Category: Machine Learning
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[R] Supercharging reinforcement learning with logic
[ad_1] Deep reinforcement learning has led to a variety of compelling results. However, performance issues, particularly relating to the data efficiency of simulation has limited it applicability in domains where simulations run more slowly. Our solution is to use a logic base framework, PyReason, as a proxy for the simulation. https://preview.redd.it/kdhpu9qraaub1.png?width=1786&format=png&auto=webp&s=8155ba38fc66bd3a2fe934b1f395351c4db68e2f We showed that…
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[N] Ensuring Reliable Few-Shot Prompt Selection for LLMs – 30% Error Reduction
[ad_1] Hello Redditors! Few-shot prompting is a pretty common technique used for LLMs. By providing a few examples of your data in the prompt, the model learns "on the fly" and produces better results — but what happens if the examples you provide are error-prone? I spent some time playing around with Open AI's davinci…