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Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It
Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. However, RL alone often leads to instability or limited gains in tool-use tasks. In our experiments, some models exhibit catas