Verifiable environments and reliability controls become central to agent training

NVIDIA-associated work highlights co-evolving model weights with restartable, observable, rubric-scored sandboxes while protecting against environment tampering and regressions. Related work addresses iterative self-distillation collapse, deceptive behavior, tool-induced safety failures, and multi-solution reinforcement learning, suggesting that evaluation and feedback design are becoming core bottlenecks for reliable agents.