Remark. Process feedback assumptions and milestone-weight discrepancy [ftip-00AE]
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    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.