diff --git a/Changelog.md b/Changelog.md
index 988dac3044..902ff269c7 100644
--- a/Changelog.md
+++ b/Changelog.md
@@ -10,7 +10,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-* fixes a few typos in the doc strings of matrix update formulae within the quasi-Newton solver.
+* fixes a few typos in the doc strings of matrix update formulae within the quasi-Newton and CG solver.
 
 ## [0.5.27] November 11, 2025
 
diff --git a/src/plans/conjugate_gradient_plan.jl b/src/plans/conjugate_gradient_plan.jl
index 1e30fb3933..d9d27e2856 100644
--- a/src/plans/conjugate_gradient_plan.jl
+++ b/src/plans/conjugate_gradient_plan.jl
@@ -52,7 +52,7 @@ The following fields from above <re keyword arguments
 $(_var(:Keyword, :X, "initial_gradient"))
 $(_var(:Keyword, :p; add = :as_Initial))
 * `coefficient=[`ConjugateDescentCoefficient`](@ref)`()`: specify a CG coefficient, see also the [`ManifoldDefaultsFactory`](@ref).
-* `restart_condition=[`NeverRestart`](@ref)`()`: specify a [restart condition](@ref cg-restart). It defaults to never restart.
+* `restart_condition=`[`NeverRestart`](@ref)`()`: specify a [restart condition](@ref cg-restart). It defaults to never restart.
 $(_var(:Keyword, :stepsize; default = "[`default_stepsize`](@ref)`(M, ConjugateGradientDescentState; retraction_method=retraction_method)`"))
 $(_var(:Keyword, :stopping_criterion; default = "[`StopAfterIteration`](@ref)`(500)`$(_sc(:Any))[`StopWhenGradientNormLess`](@ref)`(1e-8)`)"))
 $(_var(:Keyword, :retraction_method))
diff --git a/src/plans/solver_state.jl b/src/plans/solver_state.jl
index 5ad516024c..4ddddb67c4 100644
--- a/src/plans/solver_state.jl
+++ b/src/plans/solver_state.jl
@@ -367,7 +367,7 @@ For the point storage use `PointStorageKey`. For tangent vector storage use
 
 # Constructors
 
-   StoreStateAction(s::Vector{Symbol})
+    StoreStateAction(s::Vector{Symbol})
 
 This is equivalent as providing `s` to the keyword `store_fields`, just that here, no manifold
 is necessity for the construction.
diff --git a/src/solvers/conjugate_gradient_descent.jl b/src/solvers/conjugate_gradient_descent.jl
index cc7f044927..1b739f0beb 100644
--- a/src/solvers/conjugate_gradient_descent.jl
+++ b/src/solvers/conjugate_gradient_descent.jl
@@ -83,7 +83,7 @@ $(_var(:Argument, :p))
   rule to compute the descent direction update coefficient ``β_k``, as a functor, where
   the resulting function maps are `(amp, cgs, k) -> β` with `amp` an [`AbstractManoptProblem`](@ref),
   `cgs` is the [`ConjugateGradientDescentState`](@ref), and `k` is the current iterate.
-* `restart_condition::AbstractRestartCondition=`[`NeverRestart`]`)(@ref)`()`:
+* `restart_condition::AbstractRestartCondition=`[`NeverRestart`](@ref)`()`:
   rule when the algorithm should restart, i.e. use the negative gradient instead of the computed direction,
   as a functior where the resulting function maps are `(amp, cgs, k) -> corr::Bool` with `amp` an [`AbstractManoptProblem`](@ref),
   `cgs` is the [`ConjugateGradientDescentState`](@ref), and `k` is the current iterate.
@@ -97,6 +97,8 @@ $(_var(:Keyword, :vector_transport_method))
 If you provide the [`ManifoldFirstOrderObjective`](@ref) directly, the `evaluation=` keyword is ignored.
 The decorations are still applied to the objective.
 
+$(_note(:OtherKeywords))
+
 $(_note(:OutputSection))
 """
 
diff --git a/tutorials/AutomaticDifferentiation.qmd b/tutorials/AutomaticDifferentiation.qmd
index 18627c3e38..948dedb89b 100644
--- a/tutorials/AutomaticDifferentiation.qmd
+++ b/tutorials/AutomaticDifferentiation.qmd
@@ -81,7 +81,7 @@ F:  ℝ^{n+1} → ℝ,\quad F(\mathbf x) = \frac{\mathbf x^\mathrm{T}A\mathbf x}
 
 Minimizing this function yields the smallest eigenvalue $\lambda_1$ as a value and the corresponding minimizer $\mathbf x^*$ is a corresponding eigenvector.
 
-Since the length of an eigenvector is irrelevant, there is an ambiguity in the cost function. It can be better phrased on the sphere $ 𝕊^n$ of unit vectors in $ℝ^{n+1}$,
+Since the length of an eigenvector is irrelevant, there is an ambiguity in the cost function. It can be better phrased on the sphere $𝕊^n$ of unit vectors in $ℝ^{n+1}$,
 
 ```math
 \operatorname*{arg\,min}_{p ∈ 𝕊^n}\ f(p) = \operatorname*{arg\,min}_{\ p ∈ 𝕊^n} p^\mathrm{T}Ap