📄 中文摘要
RLVR（可验证奖励）作为一种新兴技术，正在逐渐取代传统的强化学习与人类反馈（RLHF）方法。RLHF虽然在许多现代前沿模型中发挥了重要作用，但其局限性逐渐显现，尤其是在奖励信号的可靠性和一致性方面。RLVR通过引入可验证的奖励机制，旨在提高模型训练的透明度和可解释性。这种方法不仅能够减少对人类反馈的依赖，还能提升模型在复杂任务中的表现。随着AI技术的不断发展，RLVR可能成为未来强化学习领域的重要方向，推动更高效和可靠的模型训练方法的实现。
序列观点 #815：RLHF的终结？可验证奖励的崛起
📄 English Summary
The Sequence Opinion #815: The End of RLHF? The Rise of Verifiable Rewards
The emergence of RLVR (Verifiable Rewards) is poised to replace traditional Reinforcement Learning from Human Feedback (RLHF) methods. While RLHF has played a significant role in many modern frontier models, its limitations are becoming increasingly apparent, particularly concerning the reliability and consistency of reward signals. RLVR aims to enhance the transparency and interpretability of model training by introducing verifiable reward mechanisms. This approach not only reduces reliance on human feedback but also improves model performance in complex tasks. As AI technology continues to evolve, RLVR may become a crucial direction in the future of reinforcement learning, facilitating the realization of more efficient and reliable model training methods.