5 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
Long-Horizon RL Needs Muon
Kai Ruan, Jinghao Lin, Tao Chen +3
Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requires weighting trajectories o…
SPO++: Stream-Aligned Policy Optimization for Asynchronous Agentic RL
Kai Ruan, Jinghao Lin, Qianshan Wei +2
Group-relative reinforcement learning waits for sibling rollouts of the same prompt, which is costly for long and variable tool-use trajectories. Single-stream Policy Optimization…
When Does Muon Help Agentic Reinforcement Learning?
Kai Ruan, Jinghao Lin, Zihe Huang +4
Muon is competitive with AdamW in large-scale pre-training, but its operating regime in reinforcement-learning post-training remains unclear. We map this regime on ALFWorld, a spar…
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Kai Ruan, Zihe Huang, Ziqi Zhou +4
Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail. We study early failure prediction and inferen…
PianoFlow: Music-Aware Streaming Piano Motion Generation with Bimanual Coordination
Xuan Wang, Kai Ruan, Jiayi Han +2
Audio-driven bimanual piano motion generation requires precise modeling of complex musical structures and dynamic cross-hand coordination. However, existing methods often rely on a…