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cs.LG2026
The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- and The Channel, Not the Content, Decides What Works
Yu Wang
Dense per-step supervision is the standard remedy for sparse-reward long-horizon LLM agents: reward the policy for predicting its next observation, which looks provably safe under…
cs.AI2026
MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation
Lu Yang, Zelai Xu, Minyang Xie +4
Large Language Model (LLM) agents have demonstrated remarkable proficiency in learned tasks, yet they often struggle to adapt to non-stationary environments with feedback. While In…