diff --git a/CHANGELOG.md b/CHANGELOG.md
index f6944653..f3979e3e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,20 +5,19 @@
 ## New Features
 
 * Added support for parametric treatment effect term in BCF [#309](https://github.com/StochasticTree/stochtree/pull/309/)
+* Added support for observation-level weights passed as data arguments to BART and BCF [#333](https://github.com/StochasticTree/stochtree/pull/333)
 
 ## Bug Fixes
 
+* Standardized multi-chain BCF handling of internal propensity models [#334](https://github.com/StochasticTree/stochtree/pull/334)
 * Fixed multi-chain BCF bugs with the parametric intercept term in R and Python [#326](https://github.com/StochasticTree/stochtree/pull/326)
 * Fixed indexing bugs for multivariate treatment BCF in Python [#326](https://github.com/StochasticTree/stochtree/pull/326)
+* Convert binary factor-valued treatments to 0/1 binary numeric treatment in `bcf()` R function [#332](https://github.com/StochasticTree/stochtree/pull/332)
 
 ## Documentation and Other Maintenance
 
 # stochtree 0.4.1
 
-## New Features
-
-## Bug Fixes
-
 ## Documentation and Other Maintenance
 
 * Converted all R man page titles to title case [#310](https://github.com/StochasticTree/stochtree/pull/310)
diff --git a/NEWS.md b/NEWS.md
index adc417a9..163505eb 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -7,6 +7,7 @@
 
 ## Bug Fixes
 
+* Standardized multi-chain BCF handling of internal propensity models [#334](https://github.com/StochasticTree/stochtree/pull/334)
 * Fixed multi-chain BCF bugs with the parametric intercept term in R and Python [#326](https://github.com/StochasticTree/stochtree/pull/326)
 * Fixed indexing bugs for multivariate treatment BCF in Python [#326](https://github.com/StochasticTree/stochtree/pull/326)
 * Convert binary factor-valued treatments to 0/1 binary numeric treatment in `bcf()` R function [#332](https://github.com/StochasticTree/stochtree/pull/332)
diff --git a/man/bcf.Rd b/man/bcf.Rd
index 6860f410..f9c700cb 100644
--- a/man/bcf.Rd
+++ b/man/bcf.Rd
@@ -40,7 +40,9 @@ that the column is ordered categorical).}
 
 \item{y_train}{Outcome to be modeled by the ensemble.}
 
-\item{propensity_train}{(Optional) Vector of propensity scores. If not provided, this will be estimated from the data.}
+\item{propensity_train}{(Optional) Vector of propensity scores. If not provided, this will be estimated from the data.
+If \code{NULL} and \code{previous_model_json} is provided with an internally estimated propensity model, that model's
+propensity estimates are re-used rather than re-fitted.}
 
 \item{rfx_group_ids_train}{(Optional) Group labels used for an additive random effects model.}
 
@@ -74,7 +76,12 @@ not compatible with cloglog link functions.}
 
 \item{num_mcmc}{Number of "retained" iterations of the MCMC sampler. Default: 100.}
 
-\item{previous_model_json}{(Optional) JSON string containing a previous BCF model. This can be used to "continue" a sampler interactively after inspecting the samples or to run parallel chains "warm-started" from existing forest samples. Default: \code{NULL}.}
+\item{previous_model_json}{(Optional) JSON string containing a previous BCF model. This can be used to "continue" a
+sampler interactively after inspecting the samples or to run parallel chains "warm-started" from existing forest
+samples. If the previous model used an internally estimated propensity score (i.e. \code{propensity_train} was not
+supplied to that run), the fitted propensity model is carried forward and re-used rather than being re-estimated.
+This ensures that multi-chain warm-starts remain consistent with the propensity scores used in the initial run.
+Default: \code{NULL}.}
 
 \item{previous_model_warmstart_sample_num}{(Optional) Sample number from \code{previous_model_json} that will be used to warmstart this BCF sampler. One-indexed (so that the first sample is used for warm-start by setting \code{previous_model_warmstart_sample_num = 1}). Default: \code{NULL}. If \code{num_chains} in the \code{general_params} list is > 1, then each successive chain will be initialized from a different sample, counting backwards from \code{previous_model_warmstart_sample_num}. That is, if \code{previous_model_warmstart_sample_num = 10} and \code{num_chains = 4}, then chain 1 will be initialized from sample 10, chain 2 from sample 9, chain 3 from sample 8, and chain 4 from sample 7. If \code{previous_model_json} is provided but \code{previous_model_warmstart_sample_num} is NULL, the last sample in the previous model will be used to initialize the first chain, counting backwards as noted before. If more chains are requested than there are samples in \code{previous_model_json}, a warning will be raised and only the last sample will be used.}