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2 results for “exploration-exploitation”
SPIN Processed News Frame: The Hype
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
A new research paper introduces SkillBoost, a three-stage framework to reduce skill overfitting in LLM agents by constraining exploration-exploitation during self-evolution of skills using prior-guided candidate generation and regression-bounded acceptance.
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arXiv Artificial Intelligence
Jul 31, 2026
SPIN Processed News Frame: The Hype
Group Entropy-Controlled Policy Optimization
Researchers propose GEPO, a new reinforcement learning method for LLM alignment that adjusts advantage signals per task group based on estimated group-level entropy to improve cross-task performance without sacrificing task-specific exploration.
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arXiv Computation and Language
Jul 21, 2026