RLHF Archives

A reward loop can optimize ticket closure instead of the intended service restoration, so evidence, permission boundaries, and review must constrain rewarded behavior.

Behaviorism and AI: How Rewards Shape Model Behavior

Published September 15, 2026 by Paul Bryant

Understand how rewards and feedback shape AI behavior. Separate reinforcement learning, human preferences, and persistent adaptation, then evaluate whether rewarded behavior actually achieves the intended outcome.

AI-action feedback crosses a gate for evidence, outcome validation, provenance, policy, scope, and authority before acceptance, correction, or rejection changes future system state.

Feedback Is the Control Plane: Preventing AI Systems From Repeating the Wrong Behavior

Published September 9, 2026 by Paul Bryant

Decide which production experiences may influence future AI behavior. Qualify observations, route corrections to the right layer, evaluate changes, and control their promotion into durable knowledge or policy.

Search Digital Thought Disruption