The September 26, 2025 paper introduces a novel reinforcement learning framework called Meta-Awareness via Self-Alignment (MASA), designed to enhance the reasoning capabilities and efficiency of large language models (LLMs) by improving their meta-awareness, or the ability to know "how to think." MASA works by creating parallel rollouts for both solution paths and meta-predictions (like predicted length and difficulty) and rewarding the alignment between these self-generated signals, thus avoiding reliance on external training sources. A more efficient variant, MASA-efficient, leverages these meta-predictions for predictive gating and early cutoff during training, substantially reducing computation time. Experimental results show that MASA significantly improves accuracy and generalization across mathematical, logical, scientific, and coding benchmarks while accelerating the training process by over 1.28 times compared to the GRPO baseline. Source: https://arxiv.org/pdf/2510.03259