Principle:Volcengine Verl Multimodal Rollout Generation

Knowledge Sources	Efficient Memory Management for Large Language Model Serving with PagedAttention Qwen2-VL: Enhancing Vision-Language Understanding
Domains	Inference, Vision_Language_Models, Distributed_Systems
Last Updated	2026-02-07 14:00 GMT

Overview

The process of generating text completions from a vision-language model given both image and text inputs, using multimodal-capable inference engines.

Description

Multimodal Rollout Generation extends standard text-only rollout to handle vision-language inputs. The key differences from text-only rollout:

• Image preprocessing: Images are processed into pixel values and positional embeddings (e.g., image_grid_thw for Qwen2.5-VL)
• Multimodal data passing: The inference engine receives both text tokens and image tensors
• 3D position IDs: VLM models like Qwen2.5-VL use 3D RoPE position embeddings that account for image spatial dimensions
• Memory management: Images consume significant GPU memory, requiring careful gpu_memory_utilization tuning

The rollout request schema includes fields for multi_modal_data (for vLLM) and multi_modal_inputs (for training forward pass).

Usage

Use multimodal rollout generation when running RL training with vision-language models. Requires:

• A multimodal-capable inference engine (vLLM or SGLang with VLM support)
• Data prepared with image columns
• Sufficient GPU memory for image processing

Theoretical Basis

Multimodal rollout extends text rollout with vision encoding:

# Abstract multimodal rollout
for batch in data:
    # Extract images and process into model format
    images = batch["images"]
    pixel_values, image_grid_thw = process_images(images)
    # Build rollout request with multimodal data
    request = RolloutRequest(
        prompt_ids=batch["input_ids"],
        multi_modal_data={"image": pixel_values},
        multi_modal_inputs={
            "pixel_values": pixel_values,
            "image_grid_thw": image_grid_thw
        }
    )
    # Generate responses
    responses = rollout_engine.generate(request)

Related Pages

Implemented By

• Implementation:Volcengine_Verl_Multimodal_Rollout_Request