Add tqdm progress bar to RayPPOTrainer for training visualization

This PR enhances the RayPPOTrainer class by implementing a progress bar
that visualizes the training process:

- Imported tqdm module in verl/trainer/ppo/ray_trainer.py (line 27)
- Added progress bar initialization in the fit() method (line 781)
- Implemented progress updates during training iterations (line 931)
- Added proper cleanup by closing the progress bar at the end of
training (line 928)

This improvement provides real-time feedback on training progress,
making it easier to monitor long-running training sessions.

---------

Co-authored-by: hoshi-hiyouga <hiyouga@buaa.edu.cn>

# What does this PR do?
This pr basically does the same thing as this
[pr](https://github.com/volcengine/verl/pull/386), but replaces the
rollout engine with sglang.

# Intro

Support Megatron checkpoint for Model, Optimizer States and RNG states,
with a new layer of abstraction: `MegatronCheckpointManager` like FSDP.
Also add checkpoint tests.

# Involved Issues and PRs

This solved issue #682 #605 , including PR #510 #634 #368 #330 . Thanks
for the great efforts of @uygnef, @ShareLer and @caaatch22 in these
contributions.

# TODOs

- [ ] Support Megatron dist checkpointing mechanism, now use
torch.save/load to store/restore model weights.
- [x] Quick: Also store hf format model.

---------

Co-authored-by: caaatch22 <mr.liumingjie@gmail.com>
Co-authored-by: Yu Feng <admin@fengyu.org>
Co-authored-by: ShareLer <sharele@163.com>

For longer tests, may check `example/grpo_trainer` folder, these 2
backends can align within 200 steps, but for more steps, megatron seems
not able to reach loss convergence.

TODO: Extended testing over longer time ranges is required to further
validate.

it should skip special tokens here. just like trl do
https://github.com/huggingface/trl/blob/fc2b041b58f6fbe766dceaec819bc5a8f9d209da/trl/trainer/grpo_trainer.py#L597


if `skip_special_tokens=False`,  completion 

```
<think>...</think><answer>....</answer>
```

will be decoded as things such as
```
<think>...</think><answer>....</answer><|im_end|><|endoftext|>
```

which will render typical `format_reward_func` mismatch

```python
r"^<think>.*?</think>\s*<answer>.*?</answer>$"
```

https://github.com/volcengine/verl/issues/703

Prevents training hangs by validating `num_key_value_heads %
ulysses_sequence_parallel_size == 0` before training.

## What does this PR do?

Add document for using vLLM 0.8 in verl

## Who can review?

@eric-haibin-lin

Adding Openmanus-RL: a llm agent rl tunning repo with verl

Add `verl` as the `framework` parameter to the SwanLab config table, so
more developers can see that this training comes from `verl`.

https://github.com/volcengine/verl/issues/680

Changes:
- Move math-verify to the optional dependencies. Now it can be installed
via `cd verl && pip install -e .[math]`
- Revert using naive verifier for math dataset. Users can switch to
math-verify or custom a new `compute_score` function.

We propose a more accurate description of DeepRetrieval.
Thanks for your awesome work!