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BIRD-RL: Scaling Agentic Reinforcement Learning over Stateful Data-Centric Environments
While agentic reinforcement learning (RL) has emerged as a promising paradigm for training small language models (SLMs), existing frameworks are predominantly tool-centric and struggle with data-centric environments. Specifically, applying tool-centric frameworks to stateful tasks like SQL debugging
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