diff --git a/benchmark/config/alfworld-template.yaml b/benchmark/config/alfworld-template.yaml
index edd0626624..be62a1bc0b 100644
--- a/benchmark/config/alfworld-template.yaml
+++ b/benchmark/config/alfworld-template.yaml
@@ -6,10 +6,10 @@ checkpoint_root_dir: placeholder
 algorithm:
   algorithm_type: grpo
   repeat_times: 8
-  loss_agg_mode: "seq-mean-token-sum"
+  loss_agg_mode: "token-mean"
   optimizer:
     lr: 1e-6
-  sample_strategy: warmup
+  sample_strategy: default
   policy_loss_fn: ppo
   advantage_fn: grpo
   kl_penalty_fn: none
diff --git a/benchmark/config/gsm8k-template.yaml b/benchmark/config/gsm8k-template.yaml
index a967589fe9..35db1831e4 100644
--- a/benchmark/config/gsm8k-template.yaml
+++ b/benchmark/config/gsm8k-template.yaml
@@ -10,7 +10,7 @@ algorithm:
     lr: 1e-5
     lr_warmup_steps_ratio: 0.0
     warmup_style: constant
-  sample_strategy: warmup
+  sample_strategy: default
   policy_loss_fn: ppo
   advantage_fn: grpo
   kl_penalty_fn: none
diff --git a/benchmark/reports/alfworld.md b/benchmark/reports/alfworld.md
index e6663478f7..e9328662a6 100644
--- a/benchmark/reports/alfworld.md
+++ b/benchmark/reports/alfworld.md
@@ -18,7 +18,7 @@ We evaluate the performance of the following methods in Trinity-RFT framework wi
 Since rLLM does not support ALFWorld environment yet, we implement this task in rLLM for comparison.
 
 In Trinity-RFT and rLLM, we respectively evaluate the performance using GRPO algorithm on this task.
-We fine-tune a `Qwen2.5-3B-Instruct` model, which has been trained on a SFT dataset, on the training tasks with GRPO and other methods. For all methods, we fix key parameters to `batch_size=32`, `repeat_times=8`, `lr=1e-6`, and `kl_coef=0.001`.
+We fine-tune a `Qwen2.5-3B-Instruct` model, which has been trained on a SFT dataset (will be released soon), on the training tasks with GRPO and other methods. For all methods, we fix key parameters to `batch_size=32`, `repeat_times=8`, `lr=1e-6`, and `kl_coef=0.001`.
 
 For better efficiency, we use 64 rollout workers in rLLM and set the `explorer.engine_num` to 4 and `explorer.runner_per_model` to 8 in Trinity-RFT.
 
diff --git a/examples/grpo_alfworld/alfworld.yaml b/examples/grpo_alfworld/alfworld.yaml
index 77ba65d555..32a491e1d7 100644
--- a/examples/grpo_alfworld/alfworld.yaml
+++ b/examples/grpo_alfworld/alfworld.yaml
@@ -56,3 +56,25 @@ trainer:
   use_dynamic_bsz: true
   max_token_len_per_gpu: 16384
   ulysses_sequence_parallel_size: 1
+# If needed, uncomment the following lines to enable SFT warmup before RFT
+# stages:
+#   - stage_name: sft_warmup
+#     mode: train
+#     algorithm:
+#       algorithm_type: sft
+#       optimizer:
+#         lr: 5e-6
+#         lr_warmup_steps_ratio: 0.0
+#         warmup_style: constant
+#     buffer:
+#       total_epochs: 1
+#       train_batch_size: 32
+#       trainer_input:
+#         experience_buffer:
+#           name: sft_warmup_dataset
+#           storage_type: file
+#           path: ${oc.env:TRINITY_SFT_DATASET_PATH}
+#           format:
+#             prompt_type: messages
+#             messages_key: 'messages'
+#   - stage_name: rft
diff --git a/examples/grpo_gsm8k/gsm8k.yaml b/examples/grpo_gsm8k/gsm8k.yaml
index b0640f089c..150cc68497 100644
--- a/examples/grpo_gsm8k/gsm8k.yaml
+++ b/examples/grpo_gsm8k/gsm8k.yaml
@@ -75,5 +75,9 @@ trainer:
 #       trainer_input:
 #         experience_buffer:
 #           name: sft_warmup_dataset
+#           storage_type: file
 #           path: ${oc.env:TRINITY_SFT_DATASET_PATH}
+#           format:
+#             prompt_type: messages
+#             messages_key: 'messages'
 #   - stage_name: rft  # leave empty to use the original configs for RFT