MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recovering these circuits typically requires a bespoke analytical pipeline for each new task. We recast circuit discovery as a reinforcement-learning problem. An agent operates over the 144 attention heads of GPT-2 small as a discrete action space; each action triggers a zero-ablation and a contrastive reward that subtracts the ablation's damage to general next-token prediction from its damage to the tar
The increasing complexity of large language models necessitates new, more automated approaches to mechanistic interpretability, moving beyond bespoke manual analysis.
This research provides a more scalable and systematic method for understanding the internal workings of AI models, crucial for safety, reliability, and further development.
Circuit discovery, previously a specialized manual process, can now be framed and potentially automated as a reinforcement learning problem, democratizing interpretability efforts.
- · AI safety researchers
- · ML engineers
- · Interpretability tool developers
- · Bespoke interpretability consultants (long term)
- · Black box AI model developers
Automated circuit discovery tools will become more common, leading to faster identification of model vulnerabilities and capabilities.
Improved interpretability will accelerate the development of more robust, transparent, and trustworthy AI systems, fostering greater adoption in sensitive applications.
A deeper understanding of AI's internal mechanisms could lead to novel architectural insights, driving the next generation of AI model design and efficiency.
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Read at arXiv cs.LG