diff --git a/src/cmdstan/command.hpp b/src/cmdstan/command.hpp
index acc659cba0..bc7f1ba54f 100644
--- a/src/cmdstan/command.hpp
+++ b/src/cmdstan/command.hpp
@@ -206,9 +206,6 @@ context_vector get_vec_var_context(const std::string &file, size_t num_chains) {
   return context_vector(num_chains, std::make_shared<dump>(dump(stream)));
 }
 
-static constexpr int hmc_fixed_cols
-    = 7;  // hmc sampler outputs columns __lp + 6
-
 namespace internal {
 
 /**
@@ -571,25 +568,31 @@ int command(int argc, const char *argv[]) {
     stream.close();
     std::vector<std::string> param_names;
     model.constrained_param_names(param_names, false, false);
+    size_t meta_cols = 0;
+    for (auto col_name : fitted_params.header) {
+      if (boost::algorithm::ends_with(col_name, "__")) {
+        meta_cols++;
+      } else {
+        break;
+      }
+    }
     size_t num_cols = param_names.size();
     size_t num_rows = fitted_params.samples.rows();
     // check that all parameter names are in sample, in order
-    if (num_cols + hmc_fixed_cols > fitted_params.header.size()) {
+    if (num_cols + meta_cols > fitted_params.header.size()) {
       msg << "Mismatch between model and fitted_parameters csv file \"" << fname
           << "\"" << std::endl;
       throw std::invalid_argument(msg.str());
     }
     for (size_t i = 0; i < num_cols; ++i) {
-      if (param_names[i].compare(fitted_params.header[i + hmc_fixed_cols])
-          != 0) {
+      if (param_names[i].compare(fitted_params.header[i + meta_cols]) != 0) {
         msg << "Mismatch between model and fitted_parameters csv file \""
             << fname << "\"" << std::endl;
         throw std::invalid_argument(msg.str());
       }
     }
     return_code = stan::services::standalone_generate(
-        model,
-        fitted_params.samples.block(0, hmc_fixed_cols, num_rows, num_cols),
+        model, fitted_params.samples.block(0, meta_cols, num_rows, num_cols),
         random_seed, interrupt, logger, sample_writers[0]);
   } else if (user_method->arg("diagnose")) {
     list_argument *test = dynamic_cast<list_argument *>(
diff --git a/src/test/interface/generated_quantities_test.cpp b/src/test/interface/generated_quantities_test.cpp
index d65788d2c5..3fadfa7d9e 100644
--- a/src/test/interface/generated_quantities_test.cpp
+++ b/src/test/interface/generated_quantities_test.cpp
@@ -20,6 +20,10 @@ class CmdStan : public testing::Test {
         = {"src", "test", "test-models", "bern_fitted_params.csv"};
     bern_fitted_params_warmup
         = {"src", "test", "test-models", "bern_fitted_params_warmup.csv"};
+    bern_optimized_params
+        = {"src", "test", "test-models", "bern_optimized_params.csv"};
+    bern_variational_params
+        = {"src", "test", "test-models", "bern_optimized_params.csv"};
     bern_fitted_params_thin
         = {"src", "test", "test-models", "bern_fitted_params_thin.csv"};
     default_file_path = {"src", "test", "test-models", "output.csv"};
@@ -34,6 +38,8 @@ class CmdStan : public testing::Test {
   std::vector<std::string> bern_data;
   std::vector<std::string> bern_fitted_params;
   std::vector<std::string> bern_fitted_params_warmup;
+  std::vector<std::string> bern_optimized_params;
+  std::vector<std::string> bern_variational_params;
   std::vector<std::string> bern_fitted_params_thin;
   std::vector<std::string> default_file_path;
   std::vector<std::string> dev_null_path;
@@ -160,6 +166,84 @@ TEST_F(CmdStan, generate_quantities_warmup) {
   ASSERT_EQ(fitted_params.samples.rows(), gq_output.samples.rows());
 }
 
+TEST_F(CmdStan, generate_quantities_after_optimization) {
+  std::stringstream ss;
+  ss << convert_model_path(bern_gq_model)
+     << " data file=" << convert_model_path(bern_data)
+     << " output file=" << convert_model_path(default_file_path)
+     << " method=generate_quantities fitted_params="
+     << convert_model_path(bern_optimized_params);
+  std::string cmd = ss.str();
+  run_command_output out = run_command(cmd);
+  ASSERT_FALSE(out.hasError);
+
+  std::stringstream msg;
+  std::string fp_path = convert_model_path(bern_optimized_params);
+  std::string gq_output_path = convert_model_path(default_file_path);
+
+  std::ifstream fp_stream(fp_path.c_str());
+  stan::io::stan_csv fitted_params;
+  stan::io::stan_csv_reader::read_metadata(fp_stream, fitted_params.metadata,
+                                           &msg);
+  stan::io::stan_csv_reader::read_header(fp_stream, fitted_params.header, &msg,
+                                         false);
+  stan::io::stan_csv_reader::read_samples(fp_stream, fitted_params.samples,
+                                          fitted_params.timing, &msg);
+  fp_stream.close();
+
+  std::ifstream gq_stream(gq_output_path.c_str());
+  stan::io::stan_csv gq_output;
+  stan::io::stan_csv_reader::read_samples(gq_stream, gq_output.samples,
+                                          gq_output.timing, &msg);
+  stan::io::stan_csv_reader::read_metadata(gq_stream, gq_output.metadata, &msg);
+  stan::io::stan_csv_reader::read_header(gq_stream, gq_output.header, &msg,
+                                         false);
+  stan::io::stan_csv_reader::read_samples(gq_stream, gq_output.samples,
+                                          gq_output.timing, &msg);
+  gq_stream.close();
+
+  ASSERT_EQ(fitted_params.samples.rows(), gq_output.samples.rows());
+}
+
+TEST_F(CmdStan, generate_quantities_after_vb) {
+  std::stringstream ss;
+  ss << convert_model_path(bern_gq_model)
+     << " data file=" << convert_model_path(bern_data)
+     << " output file=" << convert_model_path(default_file_path)
+     << " method=generate_quantities fitted_params="
+     << convert_model_path(bern_variational_params);
+  std::string cmd = ss.str();
+  run_command_output out = run_command(cmd);
+  ASSERT_FALSE(out.hasError);
+
+  std::stringstream msg;
+  std::string fp_path = convert_model_path(bern_variational_params);
+  std::string gq_output_path = convert_model_path(default_file_path);
+
+  std::ifstream fp_stream(fp_path.c_str());
+  stan::io::stan_csv fitted_params;
+  stan::io::stan_csv_reader::read_metadata(fp_stream, fitted_params.metadata,
+                                           &msg);
+  stan::io::stan_csv_reader::read_header(fp_stream, fitted_params.header, &msg,
+                                         false);
+  stan::io::stan_csv_reader::read_samples(fp_stream, fitted_params.samples,
+                                          fitted_params.timing, &msg);
+  fp_stream.close();
+
+  std::ifstream gq_stream(gq_output_path.c_str());
+  stan::io::stan_csv gq_output;
+  stan::io::stan_csv_reader::read_samples(gq_stream, gq_output.samples,
+                                          gq_output.timing, &msg);
+  stan::io::stan_csv_reader::read_metadata(gq_stream, gq_output.metadata, &msg);
+  stan::io::stan_csv_reader::read_header(gq_stream, gq_output.header, &msg,
+                                         false);
+  stan::io::stan_csv_reader::read_samples(gq_stream, gq_output.samples,
+                                          gq_output.timing, &msg);
+  gq_stream.close();
+
+  ASSERT_EQ(fitted_params.samples.rows(), gq_output.samples.rows());
+}
+
 TEST_F(CmdStan, generate_quantities_thin) {
   std::stringstream ss;
   ss << convert_model_path(bern_gq_model)
diff --git a/src/test/test-models/bern_optimized_params.csv b/src/test/test-models/bern_optimized_params.csv
new file mode 100644
index 0000000000..af5a16d698
--- /dev/null
+++ b/src/test/test-models/bern_optimized_params.csv
@@ -0,0 +1,35 @@
+# stan_version_major = 2
+# stan_version_minor = 30
+# stan_version_patch = 0
+# model = bernoulli_model
+# start_datetime = 2022-07-06 18:18:41 UTC
+# method = optimize
+#   optimize
+#     algorithm = lbfgs (Default)
+#       lbfgs
+#         init_alpha = 0.001 (Default)
+#         tol_obj = 9.9999999999999998e-13 (Default)
+#         tol_rel_obj = 10000 (Default)
+#         tol_grad = 1e-08 (Default)
+#         tol_rel_grad = 10000000 (Default)
+#         tol_param = 1e-08 (Default)
+#         history_size = 5 (Default)
+#     iter = 2000 (Default)
+#     save_iterations = 0 (Default)
+# id = 1 (Default)
+# data
+#   file = bernoulli.data.json
+# init = 2 (Default)
+# random
+#   seed = 3689276984 (Default)
+# output
+#   file = output.csv (Default)
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+#   sig_figs = -1 (Default)
+#   profile_file = profile.csv (Default)
+# num_threads = 1 (Default)
+# stanc_version = %%NAME%%3 %%VERSION%%
+# stancflags = 
+lp__,theta
+-5.00402,0.2
diff --git a/src/test/test-models/bern_variational_params.csv b/src/test/test-models/bern_variational_params.csv
new file mode 100644
index 0000000000..b0cc6da836
--- /dev/null
+++ b/src/test/test-models/bern_variational_params.csv
@@ -0,0 +1,1038 @@
+# stan_version_major = 2
+# stan_version_minor = 30
+# stan_version_patch = 0
+# model = bernoulli_model
+# start_datetime = 2022-07-06 18:19:33 UTC
+# method = variational
+#   variational
+#     algorithm = meanfield (Default)
+#       meanfield
+#     iter = 10000 (Default)
+#     grad_samples = 1 (Default)
+#     elbo_samples = 100 (Default)
+#     eta = 1 (Default)
+#     adapt
+#       engaged = 1 (Default)
+#       iter = 50 (Default)
+#     tol_rel_obj = 0.01 (Default)
+#     eval_elbo = 100 (Default)
+#     output_samples = 1000 (Default)
+# id = 1 (Default)
+# data
+#   file = bernoulli.data.json
+# init = 2 (Default)
+# random
+#   seed = 3689328985 (Default)
+# output
+#   file = output.csv (Default)
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+#   sig_figs = -1 (Default)
+#   profile_file = profile.csv (Default)
+# num_threads = 1 (Default)
+# stanc_version = %%NAME%%3 %%VERSION%%
+# stancflags = 
+lp__,log_p__,log_g__,theta
+# Stepsize adaptation complete.
+# eta = 0.1
+0,0,0,0.230025
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