<ul><li>Reinforcement learning (RL) is being used to fine-tune large language models (LLMs) to enhance reasoning abilities.</li><li>A new two-stage policy optimization framework called $A$*-PO is introduced to efficiently train LLMs for reasoning tasks.</li><li>The $A$*-PO framework approximates the optimal advantage function and eliminates the need for costly online value estimation.</li><li>$A$*-PO achieves competitive performance on mathematical reasoning benchmarks, reduces training time by up to 2$	imes$, and decreases peak memory usage by over 30% compared to existing methods.</li></ul>

Accelerating RL for LLM Reasoning with Optimal Advantage Regression

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