Think Right: Learning to Mitigate Under-Over Thinking via Adaptive, Attentive Compression
TRAAC is an online reinforcement learning method that fixes language models' inability to adjust reasoning length based on problem difficulty, known as under-adaptivity. It uses an attention-based module to dynamically prune redundant steps during training. This results in better accuracy and efficiency: the model thinks deeply for hard problems and concisely for easy ones.