{Use each reward model to provide reward signals for RL training, and evaluate the trained policy on a benchmark. A higher benchmark score indicates that the corresponding reward model is better aligned with human preferences.};
\node[box, fill=rmpurple!60,anchor=north west] (rma) at ([yshift=-0.1cm]patitle.south west) {Reward \\ Model A};
\node[box, fill=rmblue!60,anchor=north west] (rma) at ([yshift=-0.35cm]patitle.south west) {Reward \\ Model A};
\node[box, fill=rmpurple!60,anchor=north] (rmb) at ([yshift=-0.2cm]rma.south) {Reward \\ Model B};
\node[box, fill=rmpink!60,anchor=north] (rmb) at ([yshift=-0.2cm]rma.south) {Reward \\ Model B};
\node[box, fill=rmblue!60] (rl) at ([xshift=2cm]$1/2*(rma.east)+1/2*(rmb.east)$) {RL\\Training};
\node[box] (rl) at ([xshift=2cm]$1/2*(rma.east)+1/2*(rmb.east)$) {Reward \\ Scores};
\node[box, fill=rmyellow!60] (policy) at ([xshift=3cm]rl) {Policy\\LLM};
\node[box] (policy) at ([xshift=3cm]rl) {Policy\\LLM};
\node[box, fill=rmgreen!60,anchor=north west] (bencha) at ([xshift=2cm]rma.north-|policy.east) {Score of \\ Reward Model A};
\node[box, fill=rmblue!30,anchor=north west] (bencha) at ([xshift=4.0cm]rma.north-|policy.east) {Score of \\ Reward Model A};
\node[box, fill=rmgreen!60,anchor=north west] (benchb) at ([xshift=2cm]rmb.north-|policy.east) {Score of \\ Reward Model B};
\node[box, fill=rmpink!30,anchor=north west] (benchb) at ([xshift=4.0cm]rmb.north-|policy.east) {Score of \\ Reward Model B};
\node[anchor=south] at ([yshift=0.1cm]bencha.north) {\textbf{Evaluation Results}};
\node[anchor=south] at ([yshift=0.1cm]bencha.north) {\textbf{Evaluation Results}};