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CRL Steers SAE Features Token-by-Token

CRL Steers SAE Features Token-by-Token
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๐Ÿ“„Read original on ArXiv AI
#research#crl#gemma-2#interpretability#sae-steeringcontrol-reinforcement-learning-(crl)crl

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What Changed

RL policy selects SAE features per token

Why It Matters

Advances mechanistic interpretability by combining static analysis with dynamic interventions. Enables precise model steering and error diagnosis. Complements existing SAE methods for better AI understanding.

What To Do Next

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Who should care:Researchers & Academics
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