14 papers
From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory
Yishuo Cai, Xingyu Guo, Xuancheng Huang +8
Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to upda…
An Efficient, Reliable and Observable Collective Communication Library in Large-scale GPU Training Clusters
Mingjun Zhang, Xiaohe Hu, Menghao Zhang +21
Large-scale LLM training requires collective communication libraries to exchange data among distributed GPUs. As a company dedicated to building and operating large-scale GPU train…
AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards
Yiming Pan, Chengwei Hu, Xuancheng Huang +6
Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a fundamental challenge: the gen…
IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation
Bosi Wen, Yilin Niu, Cunxiang Wang +6
Instruction-following is a fundamental ability of Large Language Models (LLMs), requiring their generated outputs to follow multiple constraints imposed in input instructions. Nume…
Data-Efficient RLVR via Off-Policy Influence Guidance
Erle Zhu, Dazhi Jiang, Yuan Wang +8
Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large language models (LLMs). Current data…
IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse
Yushi Bai, Qian Dong, Ting Jiang +5
Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse…