This article about forecasting RL (Reinforcement Learning for prediction) is quite interesting.

What it does can be understood as follows:
Take a batch of historical prediction questions with known outcomes, and let the AI go back to the time point when the predictions were made.
But it must not be allowed to directly access today’s internet, otherwise it would peek at the answers.
So the author built a "time mask" environment:
Search can only retrieve materials from before that time;
Web pages are read through historical snapshots;
Financial and trend data are also limited to what was visible at the time.
Then the model is left to search for information, evaluate evidence, and output probabilities on its own.
After the actual results are revealed, it is scored using a proper scoring rule, and then RL is used to reinforce better prediction processes.
Here’s the most interesting part:
What is trained is not a single answer, but the entire sequence of prediction actions:
What to search, what to read, when to stop, how to handle conflicting evidence, and what probability to finally assign.
Applied to a prediction market, I think the first step is not to let the AI trade automatically.
Rather, it should first keep a forecast diary:
1. Probability at the time
2. Evidence used
3. Market price
4. Whether to trade
5. Subsequent outcome
6. Error classification
If a system says "60%" but over the long run it doesn't behave like 60%, then it's not yet a strategy—it's just a reason-writer.
If you also want to practice "recording a prediction → waiting for the result → calibrating yourself", you can start with small amounts or simulations, treating it as a prediction diary rather than trading advice.
My own link:

Original article here:

I think the most worthwhile takeaway is not the conclusion itself, but how it breaks down "prediction" into a process that can be trained and reviewed.