Agentic Critical Training
Agentic Critical Training (ACT) is a reinforcement learning method that trains large language models to autonomously decide which action is better by comparing an expert action against an alternative. Unlike imitation learning, ACT forces the model to develop genuine reasoning about action quality through rewards, leading to self-reflection instead of copying pre-written text.