InterleaveThinker: Reinforcing Agentic Interleaved Generation

2026-06-11 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

InterleaveThinker is the first multi-agent pipeline designed to enable existing image generators to perform interleaved generation, producing text-image sequences crucial for visual narratives and embodied manipulation. It employs a planner agent to organize input sequences and instruct the image generator, alongside a critic agent that evaluates outputs, identifies deviations, and refines instructions for regeneration. The pipeline was implemented by constructing Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k for format cold-start, then developing Interleave-Critic-RL-13k to reinforce step-wise instruction correction using GRPO. To optimize trajectories involving over 25 generator calls, accuracy reward and step-wise reward guide single-step RL. InterleaveThinker significantly improves performance across various image generators, achieving results comparable to Nano Banana and GPT-5 on interleaved generation benchmarks, and substantial gains on reasoning-based benchmarks like 4-step FLUX.2-klein (WISE and RISE).

Key takeaway

For Machine Learning Engineers developing multimodal generative AI, InterleaveThinker offers a robust pipeline to extend single-image generators into interleaved text-image sequence models. You should consider integrating planner and critic agents to enhance instruction following and output coherence, especially for visual narratives or embodied manipulation tasks, potentially achieving performance comparable to advanced models like GPT-5.

Key insights

InterleaveThinker uses a multi-agent pipeline to enable existing image generators to perform complex interleaved text-image generation.

Principles

• Agentic pipelines enhance existing generative models.
• Step-wise instruction correction improves trajectory coherence.
• Single-step RL with specific rewards guides complex trajectories.

Method

A planner agent organizes input, instructing an image generator. A critic agent evaluates outputs, identifies deviations, and refines instructions for regeneration, reinforced by GRPO with accuracy and step-wise rewards.

In practice

• Apply agentic pipelines to extend generator capabilities.
• Use planner/critic agents for complex sequence generation.
• Implement step-wise RL for long-trajectory optimization.

Topics

• Interleaved Generation
• Multi-Agent Systems
• Image Generators
• Reinforcement Learning
• Generative AI
• Multimodal Models
• Instruction Following

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.