For all their impressive capabilities, large language models (LLMs) often fall short when given challenging new tasks that require complex reasoning skills.While an accounting firm’s LLM might excel at summarizing financial reports, that same model could fail unexpectedly if tasked with predicting market trends or identifying fraudulent transactions.To make LLMs more adaptable, MIT researchers investigated how a certain training technique can be strategically deployed to boost a model’s performance on unfamiliar, difficult problems.They show that test-time training, a method that involves temporarily updating some of a model’s inner workings during…
