[fsdp, recipe] feat: add grpo reward model example using HH-RLHF dataset (#3417)

### What does this PR do?

One example of using SOTA BT reward model to train GRPO model 

- Reward Model:
[Skywork/Skywork-Reward-V2-Llama-3.1-8B](https://huggingface.co/Skywork/Skywork-Reward-V2-Llama-3.1-8B)
- Dataset:
[Dahoas/full-hh-rlhf](https://huggingface.co/datasets/Dahoas/full-hh-rlhf)


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- Wandb training curve:

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src="https://github.com/user-attachments/assets/c6dc9003-7b59-43af-8ff4-560114fe5b10"
/>

- AlpacaEval 2.0 eval results:

| Model Name |  AlpacaEval LC Win-rate | Win-rate
|:------|:-------:|:-------:|
| mistralai/Mistral-Nemo-Instruct-2407    | 42.24 | 38.68 |
| mistral12b_skyworkllama8b_grpo_hhrlhf   |  **68.20** | **68.29** |

### Design & Code Changes

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examples/grpo_trainer/run_mistral13b_skyworkrm_hhrlhf.sh

@@ -0,0 +1,50 @@
+train_files=data/full_hh_rlhf/rl/train.parquet
+test_files=data/full_hh_rlhf/rl/train.parquet # no use
+
+max_prompt_length=4096
+max_response_length=2048
+
+gen_tp=4
+n_per_prompt=5
+adv_estimator="grpo"
+
+project_name=verl_full_hh_rlhf_examples
+exp_name="grpo_mistral13B-skyworkLlama8b-hhrlhf"
+
+python3 -m verl.trainer.main_ppo \
+    algorithm.adv_estimator=$adv_estimator \
+    data.train_files="$train_files" \
+    data.val_files="$test_files" \
+    data.train_batch_size=512 \
+    data.prompt_key="prompt" \
+    data.return_raw_chat=True \
+    data.max_prompt_length=$max_prompt_length \
+    data.max_response_length=$max_response_length \
+    data.filter_overlong_prompts=True \
+    data.truncation='error' \
+    actor_rollout_ref.model.path=mistralai/Mistral-Nemo-Instruct-2407 \
+    actor_rollout_ref.actor.optim.lr=1e-6 \
+    actor_rollout_ref.actor.ppo_mini_batch_size=128 \
+    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=10 \
+    actor_rollout_ref.actor.use_kl_loss=False \
+    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=10 \
+    actor_rollout_ref.rollout.name=vllm \
+    actor_rollout_ref.rollout.n=$n_per_prompt \
+    actor_rollout_ref.rollout.gpu_memory_utilization=0.7 \
+    actor_rollout_ref.rollout.tensor_model_parallel_size=${gen_tp} \
+    actor_rollout_ref.model.enable_gradient_checkpointing=True \
+    reward_model.enable=True \
+    reward_model.model.fsdp_config.param_offload=True \
+    reward_model.model.path=Skywork/Skywork-Reward-Llama-3.1-8B \
+    reward_model.model.input_tokenizer=mistralai/Mistral-Nemo-Instruct-2407 \
+    reward_model.micro_batch_size_per_gpu=4 \
+    algorithm.use_kl_in_reward=False \
+    trainer.logger='["console","wandb"]' \
+    trainer.val_before_train=False \
+    trainer.project_name=$project_name \
+    trainer.experiment_name=$exp_name \
+    trainer.n_gpus_per_node=8 \
+    trainer.nnodes=1 \
+    trainer.save_freq=10 \
+    trainer.test_freq=-1 \
+    trainer.total_epochs=5 $@