Sam Marks
speaker
891 appearances
1 recordings
1 series
first heard Feb 2026
last heard 25 Feb
Sam Marks’s voice in public audio — every appearance, attributed to the second.
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For example, in the case of the faithful actor, there is an actor who is playing the assistant, but the actor never distorts their portrayal.
Thus, understanding how the actor will behave when in character reduces to understanding the character.
We consider agentic Reuters and narrative agency to be ambiguously agentic, and narrative agency to be ambiguously persona-like, for the reasons discussed above.
Note that these perspectives are not exhaustive.
Subheading
Why might we expect PSM to be exhaustive?
We know that randomly initialized neural networks can learn to implement agentic behaviors from scratch via reinforcement learning, RL.
For example, randomly initialized networks can learn superhuman performance at chess, Shorji, and go without any human demonstration data, Silver et al., 2017.
Because there is no pre-training prior to speak of in this setting, the agency learned by these networks is necessarily shoggoth-like rather than persona-like.
Given that we know non-persona agency can arise from scratch via RL, why would we expect agency in post-trained LLMs to be substantially persona-based?
Here we discuss two conceptual reasons.
First, that not much new is learned during LLM post-training.
Second, that reusing persona's modeling capabilities is a simple and effective way to fit the post-training objective.
We also discuss how and whether we should expect these considerations to change in the future.
Subheading Post-training as elicitation
A common view among some AI developers is that little fundamentally new is learned during post-training.
On this view, the role of post-training is mainly to elicit capabilities that the model already had.
For example, pre-trained LLMs have been trained on vast amounts of code data, including both low and high-quality code.
These pre-trained LLMs are capable of writing high-quality code, but often choose not to because high-quality code is not always the most probable.
Post-training such an LLM to write high-quality code then draws out this latent capability more so than it teaches the LLM new, strong coding capabilities from scratch.
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