Author

Jie Tang

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Jul 2026

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

This work introduces ScaleCUA, a unified framework that scales online RL for CUAs via verifiable task synthesis and efficient training, and designs VeriGen, an end-to-end framework for generating verifiable RL tasks through iterative docker interactions and a multi-agent feedback loop.

Bowen Lv, Xiao Liu, Yanyu Ren et al. · arXiv.org · 4 citations · ⚡1

Conference Open access 2026

KARL: Reinforcement Learning for LLM Agents on Multi-Turn Knowledge-Intensive Agentic Tasks

This work introduces KARL (Knowledge-Augmented Reinforcement Learning), a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions, and empowers agents to proactively decide when and what knowledge to acquire during task execution.

Xueqiao Sun, Xiao Liu, Bowen Lv et al. · Annual Meeting of the Associ... · 0 citations

Preprint Jul 2026

Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning

Single-rollout Asynchronous Optimization (SAO) is presented to address the stability and off-policy challenges in asynchronous RL and is able to train stably for one thousand steps and consistently outperform GRPO and its variants on agentic coding and reasoning benchmarks.

Zhenyu Hou, Yujiang Li, Jie Tang et al. · 10 citations · ⚡2

Preprint Aug 2026

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

TideRL is presented, a readiness-aware elastic RL system with Continuous Task Batching, Resource-Aware Ref-Actor Pipelining, and Elastic Resource Scaling that improves RL training goodput and reduces per-step training time across text-only and multi-modal agentic workloads.

Yanyu Ren, Xizheng Wang, Xiao Liu et al. · 0 citations