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Abstract DTCA improves reinforcement learning for diffusion language models by assigning token-level credit via confidence improvements and entropy calibration, boosting mathematical reasoning. Diffusion language models (DLMs) generate text through iterative denoising, enabling bidirectional context modeling and parallel token refinement. Recent studies have applied reinforcement learning (RL) to DLMs and improved their reasoning performance.
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