How does different post-training data shift a model's position along persona dimensions?

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• post-training steers models toward a particular region of persona space but only loosely tethers them to itquote0.864
  Load-bearing summary of the paper's core finding about persona stability
• Post-training steers models toward a particular region of persona space but only loosely tethers them to it, motivating work on training and steering strategies that more deeply anchor models to a coherent personaclaim0.846
  Central interpretive claim and motivation for future work
• What dimensions of persona are not captured by our extracted role vectors, and how complete is the current persona space mapping?question0.806
  Limitation question motivating future work on persona elicitation strategies
• We hypothesize that axes of persona differentiation within LLMs are likely already present in base models and inherited from the pre-training corpushypothesis0.800
  Motivated by near-identical PCs for base and instruct Gemma
• What if the concept being manipulated does not lie on a straight line in the model's representations?question0.770
  The motivating question that opens the paper and leads to the development of manifold steering.
• The Assistant Axis in instruct models mainly inherits from pre-existing helpful and harmless human personas in base models, later acquiring additional associations (such as being an AI) during post-trainingclaim0.770
  Key mechanistic claim about the developmental origin of the Assistant persona
• Can off-the-rails model behavior be attributed to their persona drifting from the Assistant?question0.769
  Motivates the multi-turn conversation drift experiments in §4
• Post-training is key to eliciting strong introspective awareness; base pretrained models do not show above-chance detectionclaim0.766
  Finding that base models have high false positives and no net positive performance.