4 citations · 11 across the 23 of their papers we have counts for
9 papers · 1 filter
Efficient Resource-Shiftable RL Post-training with Generative Rewards
Yanfeng Zhao, Haoyang Li, Sheng Lin +4
Reinforcement learning (RL) post-training with generative reward models (GenRM) has become a central recipe for improving large language models (LLMs), and it is also increasingly…
DFlow: Enabling Verifier Information Flow in Block Diffusion Speculative Decoding
Yaojie Zhang, Linfeng Zhang, Bin Cui +1
Block diffusion speculative decoding improves LLM inference efficiency by proposing a block of future tokens in parallel and verifying them with a single forward pass through the t…
QLPO: Quadrant-weighted Sampling for Length-aware Policy Optimization
Siwei Chen, Siqi Chen, Xupeng Miao +1
Recent large reasoning models often develop long chain-of-thought responses during reinforcement learning (RL), resulting in high inference latency and deployment cost. Existing me…
Adaptive Resource Management and Quality Control for Streaming Video Generation
Yifei Xia, Hao Yuan, Suhan Ling +5
Autoregressive diffusion transformers (AR-DiTs) recast video generation from an offline paradigm to a real-time streaming one: the model generates video one chunk at a time, making…
Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training
Yuming Zhou, Haoyang Li, Sheng Lin +6
Mixture-of-Experts (MoE) and reinforcement learning (RL) post-training now dominate large language model (LLM) development, yet expert load imbalance remains a critical challenge.…
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
Yujie Wang, Siwei Chen, Longzan Luo +4
Reinforcement Learning (RL) has become pivotal for improving model capabilities yet suffers from rollout efficiency bottlenecks due to the long-tail response length distribution. W…
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
Xinyi Liu, Yujie Wang, Fangcheng Fu +4
Expert parallelism is vital for effectively training Mixture-of-Experts (MoE) models, enabling different devices to host distinct experts, with each device processing different inp…
Efficient Multi-round LLM Inference over Disaggregated Serving
Wenhao He, Youhe Jiang, Penghao Zhao +4
With the rapid evolution of Large Language Models (LLMs), multi-round workflows, such as autonomous agents and iterative retrieval, have become increasingly prevalent. However, thi…
StaleFlow: Staleness-Aware Data Management for Mitigating Data Skewness in Fully Disaggregated RL Post-Training
Haoyang Li, Sheng Lin, Fangcheng Fu +6
Reinforcement learning (RL) post-training has become pivotal for enhancing the capabilities of modern large models. A recent trend is to develop RL systems with a fully disaggregat…