Remark. What the Jeffreys identity does and does not identify [ftip-008P]
Remark. What the Jeffreys identity does and does not identify [ftip-008P]
The identity Theorem [ftip-008O] is an equality inside one declared model. It says that exact gain in the optimizing reward equals a symmetric divergence from the reference law. It does not say that a larger divergence improves a different utility, nor that a training algorithm reaches the exact tilt.
The identity accounts for neither rollout and update work nor the cost of obtaining \(r\). Consequently it is not, by itself, a bound on the costed post-training potential of Definition [ftip-005M].