If you think reward-seekers are plausible, you should also think “fitness-seekers” are plausible. But their risks aren't the same.

The AI safety community often emphasizes reward-seeking as a central case of a misaligned AI alongside scheming (e.g., Cotra's sycophant vs schemer, Carlsmith's terminal vs instrumental training-gamer). We are also starting to see signs of reward-seeking-like motivations.

But I think insufficient care has gone into delineating this category. If you were to focus on AIs who care about reward in particular[1], you'd be missing some comparably-or-more plausible nearby motivations that make the picture of risk notably more complex.

A classic reward-seeker wants high reward on the current episode. But an AI might instead pursue high reinforcement on each individual action. Or it might want to be deployed, regardless of reward. I call this broader family fitness-seekers. These alternatives are plausible for the same reasons reward-seeking is—they're simple goals that generalize well across training and don't require unnecessary-for-fitness instrumental reasoning—but they pose importantly different risks.

I argue:

While idealized reward-seekers have the nice property that they’re probably noticeable at first (e.g., via experiments called “honest tests”), other kinds of fitness-seekers, especially “influence-seekers”, aren’t so easy to spot.
Naively optimizing away [...]

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Outline:

(02:32) The assumptions that make reward-seekers plausible also make fitness-seekers plausible

(05:09) Some types of fitness-seekers

(06:54) How do they change the threat model?

(10:08) Reward-on-the-episode seekers and their basic risk-relevant properties

(12:12) Reward-on-the-episode seekers are probably noticeable at first

(16:21) Reward-on-the-episode seeking monitors probably don't want to collude

(18:10) How big is an episode?

(18:55) Return-on-the-action seekers and sub-episode selfishness

(23:41) Influence-seekers and the endpoint of selecting against fitness-seekers

(25:34) Behavior and risks

(27:49) Fitness-seeking goals will be impure, and impure fitness-seekers behave differently

(28:16) Conditioning vs. non-conditioning fitness-seekers

(29:35) Small amounts of long-term power-seeking could substantially increase some risks

(30:55) Partial alignment could have positive effects

(31:36) Fitness-seekers motivations upon reflection are hard to predict

(32:58) Conclusions

(34:35) Appendix: A rapid-fire list of other fitness-seekers

The original text contained 15 footnotes which were omitted from this narration.

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First published:
January 29th, 2026

Source:
https://www.lesswrong.com/posts/bhtYqD4FdK6AqhFDF/fitness-seekers-generalizing-the-reward-seeking-threat-model

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Narrated by TYPE III AUDIO.

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Images from the article:

Flowchart showing AI training process from actions through reward-seeking to potential long-term outcomes and schemers.

Diagram showing reinforcement learning workflow with MONA and ordinary RL approaches for coding tasks.

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“Fitness-Seekers: Generalizing the Reward-Seeking Threat Model” by Alex Mallen

LessWrong (30+ Karma)