作者位次 #3/4ICLR 2026Poster· Reinforcement Learning / Deep RL
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Xinyu Qiu · Heng Jia · Zhengwen Zeng · Shuheng Shen · Changhua Meng · Yi Yang · Linchao Zhu
Xinhe Shi · Qingcheng Zeng · Weihao Xuan · Linchao Zhu
Large language models (LLMs) exhibit uneven performance across languages. In language-specific applications, practitioners often rely on target-language corpora or cross-lingual transfer to achieve better performance. However, traditional linguistic typology, commonly used as a transfer language...
Minfeng Zhu · Linxin Bao · Wei Chen · Linchao Zhu
Visual Language Models (VLMs) have become a robust foundation for document question answering. Processing long documents remains challenging due to limited context windows and computational budgets. Existing page-level retrieval methods offer a practical solution, typically encoding pages and que...
Xukun Zhu · Hang Yu · Peng Di · Linchao Zhu
The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase. However, current rollout techniques face a fundamental trade-off: token-level sampling often yields redundant trajectories that differ only...
Xuexiang Wen · Hang Yu · Linchao Zhu · Gaoang Wang
While Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising post-training paradigm for Large Language Models (LLMs), its dependency on the gold label or domain-specific verifiers limits its scalability to new tasks and domains. In this work, we propose Verifier...