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CRL Steers SAE Features Token-by-Token

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

⚑ 30-Second TL;DR

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

Key Points

  • β€’RL policy selects SAE features per token
  • β€’Tracks branch points and critic trajectories
  • β€’Syntactic features early, semantic later layers
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