Add Qwen3VLGRPOTrainer for Qwen3-VL GRPO training

[NDNM1408]

This PR adds a Qwen3VLGRPOTrainer,.

Motivation

When I was training Qwen3-VL using GRPOTrainer, I encountered the error IndexError: metadata = video_metadata[index].
While debugging, I found that the original GRPOTrainer class, when given video inputs, produced messages where video fields and fps values appeared inside "text"-type chunks with fps=None and video=None, which caused the bug.
To fix this, I created a new class that normalizes the messages before generation and prevents this error.

What this PR changes

• Adds a new class: Qwen3VLVideoGRPOTrainer(GRPOTrainer) in grpo_trainer.py.

  • Overrides _generate_single_turn only; all GRPO/DAPO logic (rewards, advantages, clipping, logging, etc.) remains unchanged.
  • Cleans and normalizes the multi-modal conversation structure while preserving:
    • {"type": "video", "video": ..., "fps": ...}
    • {"type": "text", "text": ...}
  • For each conversation in the batch:
    • Calls self.processing_class.apply_chat_template(...) directly on the Qwen3-VL conversation.
    • Sends the resulting inputs to the correct device.
    • Runs model.generate(..., generation_config=self.generation_config).
    • Splits prompt_ids and completion_ids using the prompt length, and returns them in the format expected by the GRPO pipeline.
  • Currently supports only the standard transformers.generate path and explicitly errors if use_vllm=True or use_transformers_paged=True, to keep behavior simple and predictable for this first iteration.

• Exports Qwen3VLGRPOTrainer:

  • From src/trl/trainers/__init__.py.
  • From src/trl/__init__.py.

This design keeps the change isolated and backward compatible: existing users of GRPOTrainer are unaffected, while Qwen3-VL users can opt into the specialized trainer.

Usage

Example (simplified):

from trl import Qwen3VLVideoGRPOTrainer, GRPOConfig

config = GRPOConfig(
    output_dir="qwen3vl-video-grpo",
    loss_type="dapo",
    use_vllm=False,
    use_transformers_paged=False,
    num_generations=4,
    # other GRPOConfig parameters...
)

trainer = Qwen3VLVideoGRPOTrainer(
    model="Qwen/Qwen3-VL-8B-Instruct",
    reward_funcs=my_reward_fn,       
    args=config,
    train_dataset=my_qwen3vl_video_dataset, 
)

trainer.train()