Player FM 앱으로 오프라인으로 전환하세요!
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 에피소드
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 에피소드
ทุกตอน
×플레이어 FM에 오신것을 환영합니다!
플레이어 FM은 웹에서 고품질 팟캐스트를 검색하여 지금 바로 즐길 수 있도록 합니다. 최고의 팟캐스트 앱이며 Android, iPhone 및 웹에서도 작동합니다. 장치 간 구독 동기화를 위해 가입하세요.