8 citations · 11 across the 16 of their papers we have counts for
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cs.DC2026
Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Ran Yan, Wei Fu, Jiale Li +21
LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally stati…
cs.DC2025
AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
Ran Yan, Youhe Jiang, Tianyuan Wu +7
Maximizing training throughput and cost-efficiency of RL for LLMs is essential to democratize this advanced technique. One promising but challenging approach is to deploy such a co…
cs.DC2024★ 2 cited
ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation
Zhiyu Mei, Wei Fu, Kaiwei Li +3
Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for empowering large language model (LLM) applications. Compared with the supervised training process of LL…