diff --git a/dspy/teleprompt/gepa/gepa.py b/dspy/teleprompt/gepa/gepa.py
index 5287649e5a..d7082d1ad5 100644
--- a/dspy/teleprompt/gepa/gepa.py
+++ b/dspy/teleprompt/gepa/gepa.py
@@ -136,6 +136,7 @@ class GEPA(Teleprompter):
     """
     GEPA is an evolutionary optimizer, which uses reflection to evolve text components
     of complex systems. GEPA is proposed in the paper [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://arxiv.org/abs/2507.19457).
+    The GEPA optimization engine is provided by the `gepa` package, available from [https://github.com/gepa-ai/gepa](https://github.com/gepa-ai/gepa).
 
     GEPA captures full traces of the DSPy module's execution, identifies the parts of the trace
     corresponding to a specific predictor, and reflects on the behaviour of the predictor to
@@ -175,8 +176,8 @@ def metric(
         ...
     ```
 
-    GEPA can also be used as a batch inference-time search strategy, by passing `valset=trainset, track_stats=True`, and using the
-    `detailed_results` attribute of the optimized program (returned by `compile`) to get the Pareto frontier of the batch.
+    GEPA can also be used as a batch inference-time search strategy, by passing `valset=trainset, track_stats=True, track_best_outputs=True`, and using the
+    `detailed_results` attribute of the optimized program (returned by `compile`) to get the Pareto frontier of the batch. `optimized_program.detailed_results.best_outputs_valset` will contain the best outputs for each task in the batch.
 
     Example:
     ```
diff --git a/pyproject.toml b/pyproject.toml
index 56e7d5e5aa..2350bee1db 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,7 +42,7 @@ dependencies = [
     "rich>=13.7.1",
     "numpy>=1.26.0",
     "xxhash>=3.5.0",
-    "gepa==0.0.1"
+    "gepa==0.0.2"
 ]
 
 [project.optional-dependencies]