Researchers from various institutions analyzed post-training trajectories of large language models and found that these agents tend to stick to a single training strategy throughout their execution. The team conducted experiments with different interventions, including providing experience-driven scaffolding, human guidance, and additional inference compute. Their results show that while these interventions can improve performance on certain tasks, they do not address the underlying issue of the agent's inability to spontaneously reevaluate its strategy during execution. --- Why it matters: This study matters because it highlights a crucial limitation in current post-training AI systems: their inability to adapt and change their training strategies. This finding has significant implications for researchers working on developing more robust and flexible AI models, as well as those exploring the potential of 'AI-for-AI' applications. Source: https://arxiv.org/abs/2608.19072