practitioner-guide-multi-turn-agentic-rl

Train LLM agents via multi-turn reinforcement learning by systematically optimizing environment complexity, reward signals, and policy initialization. Use curriculum learning, dense verified rewards, and domain-specific SFT for reliable agent convergence across TextWorld, ALFWorld, and SWE-Gym benchmarks.

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