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RT @omarsar0: New survey on agentic reinforcement learning for LLMs. LLM RL still treats models like sequence generators optimized in rela…
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RT @omarsar0: New survey on agentic reinforcement learning for LLMs. LLM RL still treats models like sequence generators optimized in rela…
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"text": "New survey on agentic reinforcement learning for LLMs.\n\nLLM RL still treats models like sequence generators optimized in relatively narrow settings. However, real agents operate in open-ended, partially observable environments where planning, memory, tool use, reasoning, self-improvement, and perception all interact.\n\nThis paper argues that agentic RL should be treated as its own landscape.\n\nIt introduces a broad taxonomy that organizes the field across core agent capabilities and application domains, then maps the open-source environments, benchmarks, and frameworks shaping the space.\n\nIf you are building agents, this is a strong paper worth checking out.\n\nPaper: https://t.co/qwXZNSp0ZA\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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