Two control-loop techniques for fast decision models: nested goal tiers at 10s/5s/1s/100ms, and tournament sampling when choices exceed 100
Sean Goedecke's 2026-09-18 post reports what actually worked driving a real-time agent with a decision model. Tiered goals: instead of one raw decision per tick, run periodic goal-selection loops at different intervals (strategic every 10 seconds, tactical subgoals every 5, specific targets every 1, input control every 100ms) and feed the selected goal into the lower loops, which buys computational depth on the same problem. Tournament sampling: scoring more than 100 options at once failed outright, so he scored a hundred at a time and ran a second pass over the winners, working because models are far better at relative judgments than absolute ratings. Both generalize to any router, ranker or retry-escalate layer, not just games.
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