Scale persona-space experiments to bigger models
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Idea
Scale the persona-space experiments beyond the current Qwen-2.5-7B base to larger models (e.g. Qwen-2.5-14B / 32B / 72B, or a Llama-family larger model). Captured from a Todoist one-liner ("Move to bigger models"), so the exact intended axis is underspecified; most likely it means re-running the trait-transfer / emergent-misalignment / persona-axis-characterization pipeline on a bigger base to test whether the findings hold at scale.
Why it matters
- Sibling papers (Persona Vectors; Persona Features Control EM, Mossing et al.) used GPT-4o-scale models. Showing the same persona-axis structure replicates on a substantially larger open-weights model strengthens positioning and guards against "this is a small-model artifact" reviewer pushback.
- Several current results may be size-dependent (steering strength, axis separability, EM susceptibility). Worth knowing which scale up.
Open questions
- Which model(s) and what size jump is the right next rung (14B vs 32B vs 72B)?
- Which specific experiment(s) move first — trait transfer, axis characterization, or the User Modeling / Persona Selection thread (Topic 7)?
- Compute fit: does the chosen size fit current H100/H200 pods, or does it need multi-GPU / quantization? Cost estimate before committing.
- Which results are most worth replicating at scale vs which are already convincing at 7B?
Source
Captured via my-goat Todoist auto-processor from queue/inbox/2026-05-29T01-00-05_todoist-6gjjgG5R9p4wpJVv_move-to-bigger-models.md (Todoist id 6gjjgG5R9p4wpJVv).