Diffusion language models (DLMs) generate text through iterative denoising, enabling bidirectional context modeling and parallel token refinement. Recent studies have applied reinforcement learning (R
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. However, the iterative denoising process makes credit assignment more challenging, as final outcome rewards provide limited information about how different tokens contribute to the final output. In this paper, we propose Denoising Token Credit Assignment (DTCA), a framework for token-level credit assignment in reinforcement learning for diffusion language models. DTCA estimates token contributions by measuring the improvement in prediction…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
DTCA: Denoising Token Credit Assignment for Reinforcement Learning in Diffusion Language Models was first seen on Hugging Face · Daily papers on Oct 29, 2026. Since then it has appeared in 7 checks on 1 different day. Its best position was #3 of 30, reached on Oct 29, 05:28 UTC. It dropped off the list after Oct 29, 15:28 UTC.
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#3 on Hugging Face · Daily papers, on Oct 29, 05:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 29, 2026 and has been on the list on 1 day (7 checks) since.