InterleaveThinker: Reinforcing Agentic Interleaved Generation

2026-06-11 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

InterleaveThinker is introduced as the first multi-agent pipeline designed to enable existing image generators to perform interleaved generation, producing text-image sequences. This addresses a limitation in current image generators and Unified Multimodal Models (UMMs) regarding visual narratives and embodied manipulation. The pipeline 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. Implementation utilizes Interleave-Planner-SFT-80k and Interleave-Critic-SFT-112k for format cold-start, and Interleave-Critic-RL-13k, which uses GRPO to reinforce step-wise instruction correction. To optimize trajectories involving over 25 generator calls, InterleaveThinker uses accuracy and step-wise rewards for single-step reinforcement learning. It improves performance across various image generators, achieving results comparable to Nano Banana and GPT-5 on interleaved generation benchmarks, and significantly enhancing base models on reasoning tasks like 4-step FLUX.2-klein.

Key takeaway

For Machine Learning Engineers aiming to extend existing image generators beyond single-image outputs, InterleaveThinker provides a proven multi-agent framework. You can now achieve complex interleaved text-image sequence generation, crucial for visual narratives and embodied manipulation, without retraining large models. Consider adopting a planner-critic agent architecture and single-step reinforcement learning with accuracy and step-wise rewards to significantly enhance your multimodal generation and reasoning capabilities.

Key insights

Multi-agent orchestration enables existing image generators to perform complex interleaved text-image sequence generation.

Principles

• Decompose complex generation into planning and critique.
• Reinforcement learning guides multi-step generation.
• Single-step RL optimizes long trajectories with specific rewards.

Method

A planner agent organizes input and instructs the generator; a critic agent evaluates outputs and refines instructions for regeneration, reinforced by GRPO with accuracy and step-wise rewards.

In practice

• Orchestrate existing models with multi-agent pipelines.
• Employ planner and critic agents for sequential tasks.
• Use single-step RL with accuracy/step-wise rewards.

Topics

• InterleaveThinker
• Multi-Agent Systems
• Interleaved Generation
• Image Generators
• Reinforcement Learning
• Planner-Critic Architecture
• Multimodal AI

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 Takara TLDR - Daily AI Papers.