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[D] Has interpretability research been applied to model training?

Reddit r/MachineLearning / 3/14/2026

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Key Points

  • A recent post notes that attention probes can reduce token costs by enabling early chain-of-thought exits, suggesting a potential efficiency gain.
  • It asks whether these interpretability techniques have been or could be applied to model training during pre-training or post-training with SFT/RL.
  • The discussion points to possible use cases where interpretability tools influence training procedures, not just inference.
  • The article links to a Reddit discussion and a specific post, framing this as an exploratory question within the ML community rather than reporting a finished result.

A recent X post by Goodfire (https://x.com/i/status/2032157754077691980) shows that attention probes can be used to reduce token costs by enabling early CoT exits. This seems to be an interesting use case of attention probes and I am wondering if these techniques have been applied to the models themselves during either pre-training or post-training with SFT/RL?

submitted by /u/InfinityZeroFive
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