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cs.LG2026
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…
cs.LG2026
Agentic Reinforcement Learning with Self-Distilled Reward Shaping
Ranxu Zhang, Guinan Chen, Chenshaodong +5
Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions…
cs.LG2026
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…