# Localizing RL-Induced Tool Use to a Single Crosscoder Feature

arXiv: https://arxiv.org/abs/2606.26474 · HTML: https://mlsanity.com/paper/2606.26474/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)
- Mechanistic interpretability / [Activation steering methods](https://mlsanity.com/mechinterp/activation-steering-methods/)

## Builds on (most cited first, 10 of 10)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Sparse Autoencoders Find Highly Interpretable Features in Language Models](https://mlsanity.com/paper/2309.08600/) (2023)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Toy Models of Superposition](https://mlsanity.com/paper/2209.10652/) (2022)
- [ToolRL: Reward is All Tool Learning Needs](https://mlsanity.com/paper/2504.13958/) (2025)
- [Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet](https://mlsanity.com/paper/2605.29358/) (2026)
- [Knowledge Boundary of Large Language Models: A Survey](https://mlsanity.com/paper/2412.12472/) (2024)
- [Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMs](https://mlsanity.com/paper/2602.11729/) (2026)
- [Delta-Crosscoder: Robust Crosscoder Model Diffing in Narrow Fine-Tuning Regimes](https://mlsanity.com/paper/2603.04426/) (2026)

## Built on by (most cited first, 1 of 1)

- [SFT Leaves a Reproducible Signature in Activation Geometry: Calibrating Post-Training Interpretability Against Run-to-Run Noise](https://mlsanity.com/paper/2610.17093/) (2026)

## Abstract (arXiv)

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 textitDedicated Feature Crosscoders (DFC) isolate a compact set of RL-specific features that mediate tool-calling capability in textttQwen2.5-3B. Across a 48-crosscoder hyperparameter sweep, encode-decode reconstruction improves the RL model's tool correctness by +31.1 ± 9.7 pp and passively transfers tool-calling ability to the frozen base model by +6.8 ± 5.0 pp which we call a textitcapability spillover. Our findings show that DFC partitioning concentrates RL-introduced capability into a minimal, steerable feature set that enables runtime behavioral control of agentic LLMs.