Remark. Process feedback assumptions and milestone-weight discrepancy [ftip-00AE]
AGENTDRAFTED
Appendix D.2 of Demystifying Reinforcement Learning Post-Training of Language Models[clay2026demystifying] supplements
\(r_{\rm proc}\) with an extracted-answer override and two penalties. Translating
its notation, let \(\tau =117\), \(\tau _{\rm near}=118\), and define
\[
r_{\rm base}(y)=
\begin {cases}
1.0,&\operatorname {extract}(y)=\tau ,\\
0.6,&\operatorname {extract}(y)=\tau _{\rm near},\\
r_{\rm proc}(y),&\text {otherwise}.
\end {cases}
\]
Let \(p_{\rm loop}(y)=-0.3\) when the judge flags a loop without the exact
answer, and zero otherwise. Let \(p_{\rm format}(y)=-0.5\) when the response
lacks the required boxed delimiter, and zero otherwise. Equations (7)--(8)
then give
\[
r_{\rm PRM}(y)
=\max \{0,r_{\rm base}(y)+p_{\rm loop}(y)+p_{\rm format}(y)\}.
\]
These clauses assume a reliable milestone judge, parser, near-miss choice,
loop flag, and formatting rule. They are part of the feedback definition, not
consequences of reinforcement learning. The main text describes
``exponentially increasing'' dense rewards, and Appendix D calls the weights
``exponential scaling.'' The printed vector
\((0.05,0.05,0.10,0.15,0.25)\) is neither strictly increasing at every
milestone nor a geometric progression. We preserve the exact weights and do
not infer an exponential law from that wording.