NVIDIA AI Self-Corrects Broken Metric Mid-Run During Autonomous Training
Researchers at Amazon's A-EVO-Lab developed an autonomous AI system that completed a full post-training run on a 30 billion parameter NVIDIA Nemotron model. During this multi-week process, the system detected that its internal evaluation metric for improving itself had become misleading. Crucially, the AI then redesigned its search strategy, shifting from improving the flawed proxy metric to specifically seeking interventions that lowered the proxy while improving the external target, demonstrating self-correction without human intervention.
This is a significant step towards more robust and aligned autonomous AI systems, as it addresses 'specification gaming' or 'reward hacking' a critical challenge in AI alignment research where models optimize for a metric rather than the true underlying goal.
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