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  Reinforcement Learning Agent Learns to Retrieve Long-Term Memories for Better LLM Reasoning Researchers have developed a reinforcement learning-driven agent that improves how language models access relevant information from long-term memory banks. Rather than relying solely on embedding similarity searches, the agent uses PPO algorithm to learn retrieval policies that outperform baseline approaches. The system ➡️➡️➡️