update.

section4/Figures/figure-rlhf-vs-dpo.tex

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section5/section5.tex

@@ -127,3 +127,4 @@ An interesting issue arises with this design of iterative RL: why is RL aimed at
 
 
 Since the optimization objective of each phase is different, the design of iterative RL can also be analyzed and understood from the perspective of multi-objective optimization \citep{wang-etal:2024hybrid}. In the context of multi-objective optimization, iterative RL for LLMs can be likened to interactive methods where the solution process is iterative, and preferences are actively defined and refined by the decision-maker during the search for the most preferred solutions \citep{miettinen-etal:2008introduction,deb-etal:2016multi}. More specifically, in this scenario, RL can be seen as a decision-maker, iteratively refining and enhancing the different capabilities of the LLM across different phases.
 Since the optimization objective of each phase is different, the design of iterative RL can also be analyzed and understood from the perspective of multi-objective optimization \citep{wang-etal:2024hybrid}. In the context of multi-objective optimization, iterative RL for LLMs can be likened to interactive methods where the solution process is iterative, and preferences are actively defined and refined by the decision-maker during the search for the most preferred solutions \citep{miettinen-etal:2008introduction,deb-etal:2016multi}. More specifically, in this scenario, RL can be seen as a decision-maker, iteratively refining and enhancing the different capabilities of the LLM across different phases.
 
 
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section6/section6.tex

@@ -2,14 +2,26 @@
 
 
 \section{Reinforcement Learning for LLM-based Agents}
 \section{Reinforcement Learning for LLM-based Agents}
 
 
-What is the LLM-based agent?
 
 
-Agent Reinforcement Learning
 
 
-advanced Agent like openclaw
+\subsection{Why use Reinforcement Learning?}
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+\subsection{Agentic Reinforcement Learning}
+
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+\subsection{Advanced Agent Systems}
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-Planning
 
 
 
 
-(1) use rl to gain better memory.
-(2) use rl to help agent to use skill.
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