[D] How to structure an RL solution for a forecasting problem combined with supervised learning

I’m working on a sales forecasting task with historical seasonal data. Right now, I can train a supervised model, specifically XGBoost, that works reasonably well. I was told by my supervisor to use RL on top of the supervised model predictions, but I’m having trouble understanding how reinforcement learning would actually be structured for my problem.

What part of the system would it actually adjust or control? Is this supposed to be an offline bandit, or a full RL setup with state transitions?

At the moment I only have tabular data that happened in the past, there is no influence on the future sales and model doesnt control anything. Because of this, I’m unsure whether this can meaningfully be framed as RL at all or whether people usually mean something like residual correction, bandits, or adaptive post-processing. I’m not very familiar with RL agents beyond the basics so I may be missing a something here.

I’d really appreciate examples and any ideas.