From the 1 of 10 linked papers with an AI index.
10 papers
TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling
Yanyu Ren, Xizheng Wang, Xiao Liu +8
Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with gro…
SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
Bowen Lv, Xiao Liu, Yanyu Ren +7
The paper introduces ScaleCUA, a framework that generates verifiable tasks and improves online reinforcement learning efficiency for computer use agents, achieving state-of-the-art…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
Zhenyu Hou, Yujiang Li, Jie Tang +1
Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs). Previous RL pipelines for LLMs were mostly synchronous and batch-inte…
CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents
Yujiang Li, Zhenyu Hou, Yi Jing +2
Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed.…
DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
Rui Lu, Zhenyu Hou, Zihan Wang +6
Augmenting large language models (LLMs) with browsing tools substantially improves their potential as deep search agents to solve complex, real-world tasks. Yet, open LLMs still pe…
AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework
Hanchen Zhang, Xiao Liu, Bowen Lv +11
Recent advances in large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions. However, applying reinforcem…