Reasoning in Diffusion Large Language Models is Concentrated in Dynamic Confusion Zones

• URL: http://arxiv.org/abs/2511.15208v1
• Date: Wed, 19 Nov 2025 07:59:34 GMT
• Title: Reasoning in Diffusion Large Language Models is Concentrated in Dynamic Confusion Zones
• Authors: Ranfei Chen, Ming Chen, Kaifei Wang,
• Abstract summary: We propose Adaptive Trajectory Policy Optimization (ATPO), a lightweight step-selection strategy that dynamically reallocates gradient updates to high-leverage steps without changing the RL objective, rewards, or compute budget.<n>ATPO delivers substantial gains in reasoning accuracy and training stability across benchmarks, showing that exploiting trajectory dynamics is key to advancing dLLM RL.
• Score: 3.7312377768685714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Large Language Models (dLLMs) are rapidly emerging alongside autoregressive models as a powerful paradigm for complex reasoning, with reinforcement learning increasingly used for downstream alignment. Existing trajectory-based RL methods uniformly allocate policy gradients across denoising steps, implicitly treating all steps as equally important. We challenge this assumption by analyzing trajectories with several step-level metrics: entropy-based uncertainty, Confidence-Margin (CM) uncertainty, and Rate of Entropy Change (RoEC). These reveal structured "zones of confusion": transient spikes in uncertainty and instability that strongly predict final success or failure, while most steps remain stable. We propose Adaptive Trajectory Policy Optimization (ATPO), a lightweight step-selection strategy that dynamically reallocates gradient updates to these high-leverage steps without changing the RL objective, rewards, or compute budget. Using a hybrid RoEC+CM rule, ATPO delivers substantial gains in reasoning accuracy and training stability across benchmarks, showing that exploiting trajectory dynamics is key to advancing dLLM RL.

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