[megatron] fix: remove the demising model.enable_gradient_checkpointing flags in the script (#2691)

### What does this PR do?

They were removed in https://github.com/volcengine/verl/pull/2651 ... 
@ETOgaosion 

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examples/grpo_trainer/run_deepseek7b_llm_math_megatron.sh

@@ -30,7 +30,6 @@ python3 -m verl.trainer.main_ppo --config-path=config \
     actor_rollout_ref.actor.kl_loss_coef=0.001 \
     actor_rollout_ref.actor.kl_loss_type=low_var_kl \
     actor_rollout_ref.actor.entropy_coeff=0 \
-    actor_rollout_ref.model.enable_gradient_checkpointing=True \
     actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \
     actor_rollout_ref.rollout.tensor_model_parallel_size=2 \
     actor_rollout_ref.rollout.name=vllm \

examples/ppo_trainer/run_qwen2-7b_math_gsm8k_megatron.sh

@@ -34,7 +34,6 @@ python3 -m verl.trainer.main_ppo --config-path=./config --config-name='ppo_megat
     actor_rollout_ref.ref.megatron.tensor_model_parallel_size=2 \
     critic.optim.lr=1e-5 \
     critic.model.path=Qwen/Qwen2-7B-Instruct \
-    critic.model.enable_gradient_checkpointing=False \
     critic.ppo_micro_batch_size_per_gpu=4 \
     algorithm.use_kl_in_reward=False \
     trainer.critic_warmup=0 \

examples/sglang_multiturn/geo3k/run_qwen2.5-3b_megatron_geo3k_multiturn.sh

@@ -38,7 +38,6 @@ python3 -m verl.trainer.main_ppo \
     actor_rollout_ref.actor.kl_loss_type=low_var_kl \
     actor_rollout_ref.actor.entropy_coeff=0 \
     actor_rollout_ref.actor.megatron.seed=42 \
-    actor_rollout_ref.model.enable_gradient_checkpointing=True \
     actor_rollout_ref.ref.megatron.pipeline_model_parallel_size=2 \
     actor_rollout_ref.ref.megatron.virtual_pipeline_model_parallel_size=2 \
     actor_rollout_ref.ref.megatron.context_parallel_size=2 \

examples/tuning/32b/qwen2_32B_grpo_8_h20_megatron_vllm.sh

@@ -32,7 +32,6 @@ python3 -m verl.trainer.main_ppo --config-path=config \
     actor_rollout_ref.actor.kl_loss_coef=0.001 \
     actor_rollout_ref.actor.kl_loss_type=low_var_kl \
     actor_rollout_ref.actor.entropy_coeff=0 \
-    actor_rollout_ref.model.enable_gradient_checkpointing=True \
     actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=1 \
     actor_rollout_ref.rollout.tensor_model_parallel_size=8 \
     actor_rollout_ref.rollout.name=vllm \
@@ -49,4 +48,4 @@ python3 -m verl.trainer.main_ppo --config-path=config \
     trainer.nnodes=1 \
     trainer.save_freq=-1 \
     trainer.test_freq=5 \
-    trainer.total_epochs=15 $@
+    trainer.total_epochs=15 $@

recipe/one_step_off_policy/dapo_7b_math_fsdp2_4_12.sh

@@ -90,7 +90,6 @@ python3 -m recipe.one_step_off_policy.main_ppo \
     actor_rollout_ref.ref.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
     actor_rollout_ref.rollout.log_prob_max_token_len_per_gpu=${infer_ppo_max_token_len} \
     actor_rollout_ref.model.path="${MODEL_PATH}" \
-    actor_rollout_ref.model.enable_gradient_checkpointing=True \
     actor_rollout_ref.actor.optim.lr=1e-6 \
     actor_rollout_ref.actor.optim.lr_warmup_steps=10 \
     actor_rollout_ref.actor.optim.weight_decay=0.1 \
@@ -136,4 +135,4 @@ python3 -m recipe.one_step_off_policy.main_ppo \
     trainer.nnodes="${NNODES}" \
     trainer.n_gpus_per_node="${n_gpus_training}" \
     rollout.nnodes="${NNODES}" \
-    rollout.n_gpus_per_node="${n_gpus_rollout}"
+    rollout.n_gpus_per_node="${n_gpus_rollout}"