diff --git a/.github/workflows/r_tests.yml b/.github/workflows/r_tests.yml
index b3d5ad5b1f03..43ad372a1e84 100644
--- a/.github/workflows/r_tests.yml
+++ b/.github/workflows/r_tests.yml
@@ -46,8 +46,8 @@ jobs:
         uses: actions/cache@v4
         with:
           path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-r-${{ matrix.r }}-7-${{ hashFiles('R-package/DESCRIPTION') }}
-          restore-keys: ${{ runner.os }}-r-${{ matrix.r }}-7-${{ hashFiles('R-package/DESCRIPTION') }}
+          key: ${{ runner.os }}-r-${{ matrix.r }}-8-${{ hashFiles('R-package/DESCRIPTION') }}
+          restore-keys: ${{ runner.os }}-r-${{ matrix.r }}-8-${{ hashFiles('R-package/DESCRIPTION') }}
       - uses: actions/setup-python@v5
         with:
           python-version: "3.10"
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0abe69821d14..761aef49d4dc 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -497,7 +497,8 @@ if(GOOGLE_TEST)
   configure_file(
     ${xgboost_SOURCE_DIR}/tests/cli/machine.conf.in
     ${xgboost_BINARY_DIR}/tests/cli/machine.conf
-    @ONLY)
+    @ONLY
+    NEWLINE_STYLE UNIX)
   if(BUILD_DEPRECATED_CLI)
     add_test(
       NAME TestXGBoostCLI
diff --git a/cmake/Version.cmake b/cmake/Version.cmake
index 4af6b27d6720..1a7fae3d7645 100644
--- a/cmake/Version.cmake
+++ b/cmake/Version.cmake
@@ -2,5 +2,7 @@ function(write_version)
   message(STATUS "xgboost VERSION: ${xgboost_VERSION}")
   configure_file(
     ${xgboost_SOURCE_DIR}/cmake/version_config.h.in
-    ${xgboost_SOURCE_DIR}/include/xgboost/version_config.h @ONLY)
+    ${xgboost_SOURCE_DIR}/include/xgboost/version_config.h
+    @ONLY
+    NEWLINE_STYLE UNIX)
 endfunction()
diff --git a/doc/tutorials/model.rst b/doc/tutorials/model.rst
index 97171fc3c437..d4e04262d214 100644
--- a/doc/tutorials/model.rst
+++ b/doc/tutorials/model.rst
@@ -146,7 +146,7 @@ It remains to ask: which tree do we want at each step?  A natural thing is to ad
 
 .. math::
 
-  \text{obj}^{(t)} & = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t)}) + \sum_{i=1}^t\omega(f_i) \\
+  \text{obj}^{(t)} & = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t)}) + \sum_{k=1}^t\omega(f_k) \\
             & = \sum_{i=1}^n l(y_i, \hat{y}_i^{(t-1)} + f_t(x_i)) + \omega(f_t) + \mathrm{constant}
 
 If we consider using mean squared error (MSE) as our loss function, the objective becomes