diff --git a/NEWS.md b/NEWS.md
index 6152fe738..184ddc14a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -49,6 +49,7 @@
 
 - Delay distribution discretisation now properly accounts for primary event censoring during model fitting, matching the correction already applied on the R side since v1.8.0. This improves accuracy for short delays where the observation window is large relative to the delay.
 - Left truncation of delay distributions (e.g. excluding generation times of zero) is now handled analytically rather than by zeroing and renormalising, giving more accurate PMFs near the truncation point.
+- Reduced repeated Stan work in the Rt-to-growth Newton solver by reusing the generation-time support sequence across iterations.
 
 ## Package changes
 
diff --git a/inst/stan/functions/rt.stan b/inst/stan/functions/rt.stan
index 0c1a85ce2..fb7c55975 100644
--- a/inst/stan/functions/rt.stan
+++ b/inst/stan/functions/rt.stan
@@ -94,19 +94,28 @@ void rt_lp(array[] real initial_infections_scale, vector bp_effects,
  * @param R Reproduction number
  * @param r Current estimate of the growth rate
  * @param pmf Generation time probability mass function (first index: 0)
+ * @param zero_series Precomputed vector of indices `(0, 1, ..., len - 1)`
+ * used to align `pmf` with lagged terms in the calculation; expected to have
+ * the same length as `pmf`.
  * @return The Newton step for updating r
  *
  * @ingroup rt_estimation
  */
-real R_to_r_newton_step(real R, real r, vector pmf) {
+real R_to_r_newton_step_with_support(real R, real r, vector pmf,
+                                     vector zero_series) {
   int len = num_elements(pmf);
-  vector[len] zero_series = linspaced_vector(len, 0, len - 1);
   vector[len] exp_r = exp(-r * zero_series);
   real ret = (R * dot_product(pmf, exp_r) - 1) /
     (- R * dot_product(pmf .* zero_series, exp_r));
   return(ret);
 }
 
+real R_to_r_newton_step(real R, real r, vector pmf) {
+  int len = num_elements(pmf);
+  vector[len] zero_series = linspaced_vector(len, 0, len - 1);
+  return R_to_r_newton_step_with_support(R, r, pmf, zero_series);
+}
+
 /**
  * Estimate the growth rate r from reproduction number R
  *
@@ -128,11 +137,12 @@ real R_to_r_newton_step(real R, real r, vector pmf) {
 real R_to_r(real R, vector gt_rev_pmf, real abs_tol) {
   int gt_len = num_elements(gt_rev_pmf);
   vector[gt_len] gt_pmf = reverse(gt_rev_pmf);
-  real mean_gt = dot_product(gt_pmf, linspaced_vector(gt_len, 0, gt_len - 1));
+  vector[gt_len] zero_series = linspaced_vector(gt_len, 0, gt_len - 1);
+  real mean_gt = dot_product(gt_pmf, zero_series);
   real r = fmax((R - 1) / (R * mean_gt), -1);
   real step = abs_tol + 1;
   while (abs(step) > abs_tol) {
-    step = R_to_r_newton_step(R, r, gt_pmf);
+    step = R_to_r_newton_step_with_support(R, r, gt_pmf, zero_series);
     r -= step;
   }