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Study could lead to LLMs that are better at complex reasoning

  • MIT researchers explored test-time training to enhance large language models' adaptability.
  • They found a sixfold improvement in accuracy using this method for complex tasks.
  • Test-time training involves updating model parameters with task-specific data during deployment.
  • Their work aims to make models more flexible for tasks requiring logical deduction.

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