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