Subtitle: Eliciting long-term forecasts from myopic fitness-seekers.

We’d like to use powerful AIs to answer questions that may take a long time to resolve. But if a model only cares about performing well in ways that are verifiable shortly after answering (e.g., a myopic fitness seeker), it may be difficult to get useful work from it on questions that resolve much later.

In this post, I’ll describe a proposal for eliciting good long-horizon forecasts from these models. Instead of asking a model to directly predict a far-future outcome, we can recursively:

Ask it to predict what it will predict at the next time step,
Use its prediction at the next time step to provide intermediate rewards,
Finally reward using ground truth at the last step.

This lets us replace a single distant forecast with a chain of short-horizon forecasts, each verifiable shortly after answering. I call this proposal recursive forecasting. It does have limitations: for example, it requires that developers maintain control over the reward signal at least until the final step, which makes it most useful for forecasting events that resolve well before developers are disempowered (if they are).

This post was primarily [...]

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Outline:

(01:40) The default long-term forecasting behavior

(04:08) Recursive forecasting

(07:08) When is recursive forecasting helpful?

(07:12) When we have access to (somewhat) robust ground truth rewards

(09:44) When the AIs forecast doesnt substantially affect the resolution

(11:52) When forecasts arent used as optimization targets

(12:52) When we credibly inform the AI of the setup

(13:54) Appendix A: Comparison to temporal difference learning

(16:02) Appendix B: Error tolerance

The original text contained 9 footnotes which were omitted from this narration.

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First published:
April 28th, 2026

Source:
https://blog.redwoodresearch.org/p/recursive-forecasting

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Narrated by TYPE III AUDIO.

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Images from the article:

Diagram showing iterative prediction and reward cycle from t=0 to t=T.

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