Latent methods lack task generalization and are difficult to train with autoregressive parallelization.

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• We hypothesize that degraded generalization on benchmarks like MMLU may reflect the computational demands of the tasks.hypothesis0.793
  Connecting the paper's task-difficulty findings to prior observations of weak generalization on complex QA benchmarks.
• There are fewer representations competent for N tasks than M<N tasks, so training more general models should yield fewer possible solutionshypothesis0.775
  Selective pressure toward convergence via task generality
• Autoregressive language models cannot converge to single stored patterns beyond their context window from local interactions alone.claim0.775
• Current training methods rely on loss minimization, meaning the experiential profile of training is predominantly negative across billions of parameter updatesclaim0.773
  Ethical implication about the nature of AI training experience if the thesis holds
• Achieving automatic parallelization comparable to programmer-controlled granularity is a difficult problem.claim0.763
  Scepticism about compilers fully automating granularity decisions.
• Sparse autoencoders are preferable to stronger iterative dictionary learning methods because they cannot recover features the model itself cannot accessclaim0.763
  Rationale for using simpler sparse autoencoders rather than NP-hard compressed sensing algorithms
• Parallel sub-tasks within skills and across skill families should produce parallel outputs for legibility.claim0.763
• Parallel programming needn't be terribly difficult, but 'thinking in simultaneities' as in message-passing is calculated to make it difficult.claim0.762
  Asserts that Linda's uncoupled style reduces cognitive load.