6 papers
Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
Yifan Niu, Han Xiao, Dongyi Liu +4
On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain experts to student models. Howe…
Efficient Scaling of LLM Training with Flexible Context Parallelism
Yifan Niu, Han Xiao, Dongyi Liu +2
Scaling long-context capabilities is crucial for Large Language Models (LLMs). However, real-world data contain a large number of sequences with heterogeneous lengths. Existing tra…
PiKV: KV Cache Management System for Mixture of Experts
Dong Liu, Yanxuan Yu, Ben Lengerich +1
As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache storage has become a major bottleneck…
PiCA: Pivot-Based Credit Assignment for Search Agentic Reinforcement Learning
Dongyi Liu, Yifan Niu, Qinwen Wang +2
Large Language Model (LLM)-based search agents trained with reinforcement learning (RL) have significantly improved the performance of knowledge-intensive tasks. However, existing…
Designing Large Foundation Models for Efficient Training and Inference: A Survey
Dong Liu, Yanxuan Yu, Yite Wang +5
This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and Syste…
Visual Large Language Models for Generalized and Specialized Applications
Yifan Li, Zhixin Lai, Wentao Bao +7
Visual-language models (VLM) have emerged as a powerful tool for learning a unified embedding space for vision and language. Inspired by large language models, which have demonstra…