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CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models

ArXiv|Friday, September 11, 2026|10 days ago|Blake Olson

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Why It Matters

Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use tasks.

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Key Claims

While Reinforcement Learning (RL) has recently been applied to enhance DLMs, standard RL approaches suffer from an exploration bottleneck.
To address this, we inject reasoning priors from a stronger teacher model to guide RL exploration.
In this paper, we introduce CanvasAnneal, a curriculum-guided diffusion RL framework.

Original description

Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use tasks. While Reinforcement Learning (RL) has recently been applied to enhance DLMs, standard RL approaches suffer from an exploration bottleneck. To address this, we inject reasoning priors from a stronger teacher model to guide RL exploration. In this paper, we introduce CanvasAnneal, a curriculum-guided diffusion RL framework. During the initial RL phase, we warm-start exploration by injecting teacher-generated reasoning traces into the init...

arxiv cs.LG publisher:arxiv tier:research

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Publisher: arxiv.org

ID: http://arxiv.org/abs/2609.13060v1 · Indexed 7 days ago