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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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