Soft-Optimization Cap

Problem

Aggressive optimisation pushes the agent toward action regions where the objective and the true preference diverge most. The 0.001-quantile of action-space (the extreme argmax tail) is the region most likely to contain degenerate maxima the designer never anticipated. Capping how hard the agent optimises trades a little expected score against a large amount of safety from specification gaming.

Solution

Following Taylor's quantilizers: define a base distribution over actions (the agent's prior over reasonable moves). To pick an action, sample from the top q-quantile of that distribution ranked by the inferred objective. The classic bound: a q-quantilizer's expected cost under any bounded utility is at most 1/q times the cost of the base distribution. In practice for LLM agents: take top-k sampling on the planner, or set a satisficing threshold and accept the first action that clears it. Cap is a tuned parameter, not optimisation.

When to use

• The agent's inferred objective is plausibly mis-specified at the tail.
• A reasonable base distribution of human-endorsed actions exists.
• Some loss of expected score is acceptable in exchange for tail safety.