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Great work! I have two questions regarding the reward design:

1. How do you balance the different reward components? I assume it's through trial and error, but I'm particularly interested in:

   • The scale of each reward component
   • How numerical adjustments impact the RL training process
   • The relative weights between different rewards

2. Regarding the F1 score calculation: Is it computed based on the number of entries in your graph? I'm curious about the granularity of reward design, as different reward components seem to operate at different levels of detail:

   • Format reward appears to be one-dimensional
   • F1 reward seems to be a composite metric derived from multiple sub- level data points

This granularity difference in reward design could potentially affect the training dynamics. Would love to hear your thoughts on handling these different scales of feedback. Thanks :)