Inpainting-Guided Policy Optimization for Diffusion Large Language
  Models
Authors: name: Siyan Zhao, name: Mengchen Liu, name: Jing Huang, name: Miao Liu, name: Chenyu Wang, name: Bo Liu, name: Yuandong Tian, name: Guan Pang, name: Sean Bell, name: Aditya Grover, name: Feiyu Chen

Masked diffusion large language models (dLLMs) are emerging as promising
alternatives to autoregressive LLMs, offering competitive performance while
supporting unique generation capabilities such as inpainting. We explore how
inpainting can inform RL algorithm design for dLLMs. Aligning LLMs with
reinforcement learning faces an exploration challenge: sparse reward signals
and sample waste when models fail to discover correct solutions. While this
inefficiency affects LLMs broadly, dLLMs offer a distinctive opportunity--their
inpainting ability can guide exploration. We introduce IGPO (Inpainting Guided
Policy Optimization), an RL framework that strategically inserts partial
ground-truth reasoning traces during online sampling. Unlike providing full
solutions, inpainting steers exploration toward promising trajectory spaces
while preserving self-generated reasoning, bridging supervised fine-tuning and
reinforcement learning. We apply IGPO to group-based optimization methods such
as GRPO, where exploration failures cause zero advantages and gradients. IGPO
restores meaningful gradients while improving sample efficiency. We also
propose supervised fine-tuning on synthetically rewritten concise traces that
better align with dLLM generation patterns. With additional techniques
including entropy-based filtering, our training recipe yields substantial gains
across three mathematical benchmarks--GSM8K, Math500, and AMC--achieving new
state-of-the-art results for full-attention masked dLLMs.