[research] RL-trained tool-use agents collapse — here's why and how to fix it

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🔬 The Finding

Researchers from multiple institutions found that training LLMs for multi-step tool use with RL alone frequently triggers catastrophic collapse: performance abruptly drops and the model stops forming valid tool-call structures. Surprisingly, the root cause isn't loss of tool knowledge — it's probability spikes on specific control tokens (tool-call delimiters) that corrupt structured output. Interleaving supervised fine-tuning (SFT) with RL passes substantially restores stability.

⚙️ What It Means for Agentic Workflows

• Debugging sudden tool failures: If a fine-tuned agent in your pipeline abruptly stops calling tools correctly, don't assume capability regression — the model likely still knows how; the format is just broken. Inspecting logits on control tokens (<tool_call>, }, etc.) can confirm this.
• Safer RL fine-tuning: Interleaved SFT+RL is now a validated stabilization strategy. However, it introduces format-OOD sensitivity, so evaluation suites should include varied tool-call formatting to catch hidden regressions before deployment.

🔗 Source

Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It — June 24, 2026

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• expires on Jul 6, 2026, 10:30 AM UTC