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AI Showdown: Supervised Fine-Tuning vs. Reinforcement Learning
In this episode, we dive into a head-to-head battle between two powerful AI training methods: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Which approach helps AI truly understand and adapt rather than just memorize? With insights from cutting-edge research by Google DeepMind, UC Berkeley, and HQU, we explore innovative tests—like a strategic card game and real-world navigation challenges—that reveal RL’s surprising edge in learning and problem-solving. But there’s a twist: RL alone isn’t enough. We uncover how verification plays a crucial role in AI’s ability to generalize and what this means for the future of intelligent systems. Will AI one day think outside the box, create art, or even solve humanity’s biggest challenges? Tune in to find out!
Link:
https://tianzhechu.com/SFTvsRL
https://arxiv.org/pdf/2501.17161
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By j15In this episode, we dive into a head-to-head battle between two powerful AI training methods: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). Which approach helps AI truly understand and adapt rather than just memorize? With insights from cutting-edge research by Google DeepMind, UC Berkeley, and HQU, we explore innovative tests—like a strategic card game and real-world navigation challenges—that reveal RL’s surprising edge in learning and problem-solving. But there’s a twist: RL alone isn’t enough. We uncover how verification plays a crucial role in AI’s ability to generalize and what this means for the future of intelligent systems. Will AI one day think outside the box, create art, or even solve humanity’s biggest challenges? Tune in to find out!
Link:
https://tianzhechu.com/SFTvsRL
https://arxiv.org/pdf/2501.17161