<ul><li>The VAPO framework has shown empirical success in improving efficiency and reliability of reinforcement learning for long CoT reasoning tasks with LLMs.</li><li>VAPO addresses challenges like value model bias, varying sequence lengths, and sparse reward signals, leading to state-of-the-art performance.</li><li>While VAPO has practical benefits, understanding its theoretical foundations and limitations is crucial for future advancements.</li><li>This paper explores VAPO theoretically, identifying areas for further investigation to enhance reasoning agents' robustness and generality.</li></ul>

Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective

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