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arXiv AI By Ziang Cui, Mengran Yu, Chenyu Shi, Yingxuan Shi, Tianjiao Li

HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning

Read the original on arXiv AI →

The paper introduces Sand-Glass, a benchmark for evaluating translation under syllable-level duration constraints, and proposes Homura, a reinforcement learning framework that optimizes the trade-off between semantic preservation and temporal compliance. Homura uses a constrained reinforcement learning objective with a dynamic syllable-ratio reward to effectively control output length. Experimental results show that Homura outperforms strong baselines, achieving precise length control while maintaining semantic adequacy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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