diff --git a/eval_protocol/pytest/evaluation_test.py b/eval_protocol/pytest/evaluation_test.py
index 2dd4dbc0..561fb14d 100644
--- a/eval_protocol/pytest/evaluation_test.py
+++ b/eval_protocol/pytest/evaluation_test.py
@@ -30,6 +30,7 @@
     Status,
 )
 from eval_protocol.pytest.default_dataset_adapter import default_dataset_adapter
+from eval_protocol.pytest.default_mcp_gym_rollout_processor import MCPGymRolloutProcessor
 from eval_protocol.pytest.default_no_op_rollout_processor import NoOpRolloutProcessor
 from eval_protocol.pytest.rollout_processor import RolloutProcessor
 from eval_protocol.pytest.types import (
@@ -585,7 +586,9 @@ def _log_eval_error(status: Status, rows: Optional[List[EvaluationRow]] | None,
                         exception_handler_config=exception_handler_config,
                     )
 
-                    for i in range(num_runs):
+                    async def execute_run(i: int, config: RolloutProcessorConfig):
+                        nonlocal all_results
+
                         # Regenerate outputs each run by deep-copying the pristine dataset
                         # so model responses are not reused across runs.
                         run_id = generate_id()
@@ -728,6 +731,18 @@ async def _collect_result(config, lst):
                                     r.eval_metadata.status = Status.eval_finished()
                             active_logger.log(r)
 
+                    tasks = []
+                    for i in range(num_runs):
+                        tasks.append(asyncio.create_task(execute_run(i, config)))
+
+                    # if rollout_processor is McpGymRolloutProcessor, we execute runs sequentially since McpGym does not support concurrent runs
+                    # else, we execute runs in parallel
+                    if isinstance(rollout_processor, MCPGymRolloutProcessor):
+                        for task in tasks:
+                            await task
+                    else:
+                        await asyncio.gather(*tasks)
+
                     # for groupwise mode, the result contains eval otuput from multiple completion_params, we need to differentiate them
                     # rollout_id is used to differentiate the result from different completion_params
                     if mode == "groupwise":