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
arXiv:2608. 16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy.
By Siddarth Venkatraman, Matthieu Dinot, Laurence Aitchison
arXiv:2607. 14506v1 Announce Type: cross Abstract: While reinforcement learning with verifiable rewards (RLVR) is widely used to improve the reasoning capabilities of large language models (LLMs), the generalizability of the resulting models remains poorly understood.
By Yuxuan Zhu, Rohan Alur, Daniel Kang
arXiv:2606. 01934v1 Announce Type: new Abstract: Large language models achieve remarkable performance via extended chain-of-thought (CoT) reasoning, yet this lengthy process incurs substantial inference overhead.
By Minghui Zheng, Hongxu Chen, Huimin Ren, Hongsheng Xin, Xiaoyang Qu, Ze Wang, Shuling Yang, Ziyu Peng, Kaike Zhang, Pan Zhou, Kun Zhan
arXiv:2607. 15200v1 Announce Type: cross Abstract: Reinforcement learning has proven effective for improving reasoning in large language models, but extending it to Masked Diffusion Language Models (MDLMs) remains challenging due to the intractability of the log-likelihood estimation.
By Haran Raajesh, Kulin Shah, Adam Klivans, Philipp Kr\"ahenb\"uhl
arXiv:2607. 04728v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which inevitably results in off-policy training data.
By Yu Li, Xiuyu Li, Mingyang Yi, Jiaxing Wang, zhangliangxu, Zhaolong Xing, Zhen Chen
arXiv:2511.23271v2 Announce Type: replace
Abstract: Long system prompts are widely used to steer Large Language Models (LLMs), but repeatedly processing them at inference time is inefficient and cons...
By Jiancheng Dong, Pengyue Jia, Jingyu Peng, Maolin Wang, Yuhao Wang, Lixin Su, Xin Sun, Shuaiqiang Wang, Dawei Yin, Xiangyu Zhao
Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which inevitably results in off-policy training data. To resolve this, Importance sampling (IS) is proposed, while the token-level ratios compound over long sequences, causing severe variance exploded.
arXiv:2605.27293v2 Announce Type: replace
Abstract: Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existin...
By Shijin Gong, Erhan Xu, Kai Ye, Giulia Livieri, Francesco Quinzan, Chengchun Shi
The paper proposes a modular tokenizer framework for multilingual large language models, allowing the creation of language‑specific subtokenizers that match monolingual compression quality. It introduces a pretraining strategy that samples these subtokenizers to limit predictions to relevant vocabularies, enabling efficient training and inference. This approach reduces memory usage and speeds up inference without compromising performance.
By Franck Signe, Hippolyte Pilchen, Fran\c{c}ois Yvon, \'Edouard Grave
arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.
By Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee
arXiv:2607. 17524v1 Announce Type: cross Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness prediction task.
By Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu, Robin Jia