arXiv:2606.26474v1 Announce Type: new Abstract: Fine-tuning through RL reshapes the internal representations of language models to enable agentic behaviors such as tool use, yet the mechanistic basis of these changes remains poorly understood. While RL substantially improves structured tool-call generation, it is unclear which features emerge, which are preserved, and whether identified features can be leveraged for retraining-free behavioral control. In this work, we show that $\textit{Dedicated Feature Crosscoders (DFC)}$ isolate a compact set of RL-specific features that mediate tool-callin
The rapid advancement in AI agentic capabilities, particularly around tool use, creates an urgent need to understand and control their underlying mechanisms.
This research provides a fundamental insight into how AI agents develop and utilize advanced behaviors, potentially enabling more robust and controllable AI systems.
We gain a mechanistic understanding of how RL induces tool use in language models, shifting from black-box behavior to identifiable and manipulable features.
- · AI researchers
- · AI safety organizations
- · Developers of AI agents
- · Companies using AI for automation
- · Malicious AI actors
- · Uncontrollable AI systems
This work enables more precise control and debugging of AI agent behavior.
The ability to isolate and leverage specific features could lead to more efficient and specialized AI models for tool use.
This deeper understanding may accelerate the development of highly autonomous and reliable AI agents for complex tasks, potentially collapsing white-collar workflows.
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Read at arXiv cs.LG