MIT method doubles reasoning LLM training speed by using idle GPU time
MIT researchers found a way to leverage idle GPU cycles during reasoning model training by automatically training a smaller, faster model to predict the larger model's outputs. The larger model then verifies these predictions, cutting its workload.
The system trains and deploys the smaller model adaptively, kicking in only when processors are idle. Tested on multiple reasoning LLMs, it doubled training speed while preserving accuracy, potentially cutting costs for applications like financial forecasting and power grid risk detection.
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