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From AI Assistants to Code Wizards: Can Reinforcement Learning Outcode GPT Models?
Manage episode 385901736 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/from-ai-assistants-to-code-wizards-can-reinforcement-learning-outcode-gpt-models.
Large language models can generate highly fluent and but inaccurate text. But Reinforcement learning systems can be far more accurate and cost-effective.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #rl, #reinforcement-learning, #gpt-models, #openai, #artificial-intelligence, #llm-hallu, #future-of-ai, and more.
This story was written by: @mlodge. Learn more about this writer by checking @mlodge's about page, and for more stories, please visit hackernoon.com.
Reinforcement learning systems can be far more accurate and cost-effective than large language models because they learn by doing. Large language models can write code suggestions and so much has been made of their usefulness in unit testing. However, because LLMs trade accuracy for generalization, the best they can do is suggest code to developers, who then must check the code for effectiveness.
316 פרקים
Manage episode 385901736 series 3474148
This story was originally published on HackerNoon at: https://hackernoon.com/from-ai-assistants-to-code-wizards-can-reinforcement-learning-outcode-gpt-models.
Large language models can generate highly fluent and but inaccurate text. But Reinforcement learning systems can be far more accurate and cost-effective.
Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #rl, #reinforcement-learning, #gpt-models, #openai, #artificial-intelligence, #llm-hallu, #future-of-ai, and more.
This story was written by: @mlodge. Learn more about this writer by checking @mlodge's about page, and for more stories, please visit hackernoon.com.
Reinforcement learning systems can be far more accurate and cost-effective than large language models because they learn by doing. Large language models can write code suggestions and so much has been made of their usefulness in unit testing. However, because LLMs trade accuracy for generalization, the best they can do is suggest code to developers, who then must check the code for effectiveness.
316 פרקים
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