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Stop Repeating Mistakes: How LLMs Can Learn from Feedback in Real Time
Large Language Models (LLMs) are incredibly powerful, yet they struggle with one subtle weakness—complex, multi-step reasoning. Ask a model to solve an Olympiad-level math question or a competitive programming puzzle, and its first attempt is often wrong. The challenge isn’t generating an answer—it’s learning from failure effectively. Humans learn from mistakes. We rarely repeat the same error twice because we internalize what went wrong. Could LLMs do something similar? Could they learn from feedback while they’re being tested—improving with every iteration? ...