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Adhyayan
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2026-03-04 01:04:41
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Adhyayan Adhyayan Tuesday, July 15, 2025 Missing learning loop for LLMs Andrej's take on RL with chatgpt magic. How humans understand touches on the need for this elaboration. 1. RL is powerful, but not the full story RL is gaining traction and will continue to generate useful results , particularly because it’s more leveraged than traditional supervised fine-tuning (SFT). But it has limitations —particularly with long-horizon tasks (tasks that take a long time or many steps). The standard RL approach—rewarding or punishing actions based on final scalar feedback —is very lossy , especially when the task is long and complex. “You're really going to do all that work just to learn a single scalar outcome at the very end?” 2. Human learning isn’t like that Humans don't just get a reward at the end; they reflect . After doing something, we think: What went well? What didn’t? What could I do differently? These explicit lessons are stored consciousl...
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