Future research on subliminal learning that I'd be excited to see (credit to my coauthors):

• Robustness to paraphrasing
• Generally, clarifying cross-model transmission: when does it happen?
  • Connect subliminal learning to Linear Mode Connectivity (h/t Alex Dimakis)
  • Can subliminal learning occur when the base models had different inits but are trained to be similar? (Clarifies whether init is what matters)
• Develop theory
  • Quantify transmission via random matrix theory (build off equation 2 in the paper). Are there nice relationships lurking there (like d_vocab : d_model)?
  • Can we get theory that covers the data filtering case?
• Figure out what can and can’t be transmitted
  • Backdoor transmission
  • Information-theoretic limits
  • Dependence on tokenization
• Subtle semantic transmission: what about cases that aren't subliminal learning but are very hard to detect? Connect to scalable oversight and/or control.
• Adversarially-constructed subliminal learning datasets (no teacher) (compare with "clean label" data poisoning literature)