Plan, Edit, Refine: Joint RL Training of LLM Agents and Diffusion Editors

Compositional text-to-image generation requires satisfying multiple attribute constraints simultaneously, a task that single-pass generation routinely fails on. Two recent lines of work address this from opposite ends: agentic iterative refinement trains only the language planner while freezing the image generator, and unified policy optimization trains language and visual generation components but in a single generation round. We present NEXUS, a work-in-progress framework combining both properties: iterative, multi-round refinement with joint RL training of both the LLM planner (Qwen2.5-7B) and the reference-conditioned diffusion editor (FLUX.1-Kontext-dev) via shared GRPO reward. A dual-channel Bridge routes continuous LLM hidden states and discrete text instructions to the editor's conditioning mechanism. Empirically, using three-seed means on the full 553-prompt GenEval prompt set scored with our Qwen-VQA compositional score rather than the official GenEval evaluator: (1) zero-shot Gemini with the same iterative refinement loop reaches 0.740 and serves as the matched baseline; (2) NEXUS full reaches 0.734 at step 50 and 0.780 at step 100, surpassing this matched Gemini baseline by +4.0 pp at step 100; and (3) planner-only and no-Bridge variants do not exceed the matched Gemini baseline at step 100, highlighting the importance of the full co-adaptive system.