
\section{Conclusions and Future Directions}
In this paper, we have introduced the fundamental concepts of RL from the perspective of LLM research. We began with basic RL formulations and algorithms, including policy gradients, advantage estimation, importance sampling, and reward modeling, and then discussed recent advances in RL. We further extended the discussion to reasoning models, agentic systems, and multimodal models, showing how RL has evolved from a general policy optimization framework into a key paradigm for enhancing LLM reasoning, interactive decision-making, and multimodal generation capabilities.

While RL has become well established in LLM training and has achieved remarkable results, several promising directions remain for future exploration:

\begin{itemize}
    \item \textbf{RL for pre-training.} Most existing studies apply RL during the post-training stage, while its role in large-scale pre-training remains relatively underexplored. One promising direction is to investigate how reward-driven learning can be integrated into pre-training to shape model capabilities earlier in the training pipeline. Recent studies have begun to demonstrate the feasibility of this direction \citep{dong-etal:rl}, but how to design maintain stable optimization at pre-training scale remains an open challenge.

    \item \textbf{More efficient RL training.} Recent methods, such as GRPO, have simplified RL training by removing components such as the critic model. However, RL still relies heavily on online sampling, which introduces substantial computational and time costs. Improving sampling efficiency and reducing the overall training cost therefore remain important directions for future research.

    \item \textbf{Predicting the scaling limits of RL.} The final gains from RL depend on many factors, including model capability, training data, reward quality, and optimization settings. In practice, we often need to complete expensive RL runs before knowing the final performance. Developing methods to predict the potential gains and performance limits of RL before full-scale training could significantly improve the efficiency of model development.
    
    \item \textbf{Continual and self-evolving RL.} Most current RL pipelines optimize a model on a fixed training distribution and stop once training is completed. One more ambitious direction is to enable models to continuously learn from their own interactions, feedback, and accumulated experiences after deployment. This requires new mechanisms for experience selection, memory and skill evolution, and stable policy updating without catastrophic forgetting.
\end{itemize}

% future work
% 强化学习作为一种预训练方式
% 高效强化学习方法
% 如何预测RL训练的上届？RL训练往往和Model performance, data, 参数有关系。通常，我们仅仅只能通过完整的训练才能的得知最后的性能。