← Back to all news
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.

Related stories

arXiv AI
Sep 7

Reinforcement Learning for improving Large Language Models' Catalan text simplification capabilities

The paper explores using reinforcement learning to enhance automatic text simplification for low‑resource languages, focusing on Catalan. It introduces a new reward function that blends the SARI metric with penalty terms, and applies Group Relative Policy Optimization (GRPO) to fine‑tune the IberianLLM‑7B‑Instruct model on the ASSET dataset. Post‑training, the model shows improved simplification performance on two Catalan benchmarks and reduces prior negative behaviors, though cross‑lingual transfer from English, Spanish, and Catalan translations of ASSET does not yield significant gains on an out‑of‑domain benchmark.

By Arnau Ayguad\'e Domingo, Stefan Bott, Horacio Saggion
llmsnlpreinforcement-learningfine-tuningbenchmarks
More like this →
arXiv AI
Jun 9

Rewrite to Translate, Translate to Reward: Reinforcement Learning for Source Rewriting in Machine Translation

arXiv:2606. 08011v1 Announce Type: cross Abstract: Although directly prompting off-the-shelf Large Language Models (LLMs) to generate meaning-preserving source rewrites can effectively enhance Machine Translation (MT) quality, doing so requires manually tuning prompts for different MT models.

By Boxuan Lyu, Haiyue Song, Zhi Qu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
llmsnlpreinforcement-learning
More like this →
arXiv Computation and Language
Sep 21

Dynamic Lagging using Stable-Prefix Training for Simultaneous Translation

The paper introduces a training strategy for cascaded simultaneous speech translation that allows the system to dynamically decide how much of the source prefix to translate. By fine‑tuning a large language model (Qwen3‑8B) on stable prefixes—pairs of source prefixes and the longest shared translation with the full sentence—the authors enable contextual read‑write decisions beyond fixed wait‑k or target‑suffix deletion. Experiments on English‑to‑German, Japanese, and Chinese demonstrate that stable prefixes improve the quality‑latency tradeoff across various test sets.

By Hieu Hoang, Amittai Axelrod, Matt Post
llmsnlpfine-tuning
More like this →
arXiv AI
Oct 21

TrajEvolve: Trajectory Evolution for Reinforcement Learning with Hindsight Credit Assignment

TrajEvolve introduces a trajectory evolution paradigm with hindsight credit assignment to improve reinforcement learning for large language models on long-horizon tasks. It refines low-quality steps in historical trajectories to expose the policy to high-quality compositions that are rarely sampled, and reformulates step-level continuous credit assignment as a binary classification problem to identify redundant steps. Experiments on two datasets show that this approach outperforms existing methods.

By Yi Wen, Hao Chen, Wanyu Wang, Maolin Wang, Pengyue Jia, Derong Xu, Huize Tan, Yingyi Zhang, Wenlin Zhang, Weihong Luo, Xiku Du, Xiangyu Zhao
llmsreinforcement-learning
More like this →