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This is a Quantitative Study study (also classified as: Experimental Study).

September 30, 2025Open Access

Inpainting-Guided Policy Optimization for Diffusion Large Language Models

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IGPO enhances exploration by using inpainting to guide policy optimization in reinforcement learning models.
Substantial improvements were observed with IGPO, achieving new state-of-the-art results on benchmarks like GSM8K.
This framework effectively addresses sparse reward signals, increasing sample efficiency during training.
Supervised fine-tuning on concise traces further aligns model behavior with dLLM generation capabilities.

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This approach improves sample efficiency and exploration in large language models, demonstrating significant gains on multiple benchmarks.

Zhao et al. (2025) studied this question.

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This approach improves sample efficiency and exploration in large language models, demonstrating significant gains on multiple benchmarks.