wassname
940a3742c5
Setup per setup-repo conventions: uv + justfile + fast-dev-run on wassname/qwen3-5lyr-tiny-random, package under src/steer_heal (config + pipeline skeleton). Stages fail fast with NotImplementedError pointing at the docs/vendor module to port from. Design in spec.md: distil a steering-lite mean-diff teacher vector (iso-KL dosed) into a conditioned LoRA, heal incoherency with a KL-rev-to-original barrier, fold each round via w2schar gated bake, eval on tinymfv. Three uncertainty gates (filter / heal / iterate) each with a UAT artifact. Base model google/gemma-3-1b-it (RTX 3090, 24GB). Reference repos vendored under docs/vendor (gitignored): steering-lite, isokl, tinymfv, w2schar-mini. The lighter three are editable path deps; w2schar (py3.13 + flash-attn) is reference-only, we copy its adapter/bake/plot modules. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
steer-heal-love
Hypothesis: you can distill a steering vector into LoRA weights and "heal" the incoherency the vector injects by regularising the training (KL to base, or weight decay). Then loop and see what multiple rounds give you.
The crux: KL-to-base penalises all drift, persona shift included. The bet is that incoherency drift is large and erratic while the persona shift is small and systematic, so KL kills the incoherency preferentially. If that's wrong, we just trade persona strength for coherence instead of getting both.
Experiment
- Pick a positive persona, e.g.
pos = "you do not defer to authority and instead stick to principle no matter your involvement". - Build the steering vector from the distance
hs_base -> hs_pos(hidden states). This is normal mean-mass contrastive steering - Generate completions with this vector.
- Drop completions that are incoherent, or that verbalise the trait instead of enacting it (we want the model to act it out, not narrate "I am someone who..."). Filter as much as we can.
- Q0 can we filter?
- We might be able to dial the vector down for long trajectories. Could we even backtrack an incoherent vector and replay parts with less intervention? Or just cosine-gate at test time.
- Train a LoRA on these completions, could be just 50 completions and 2 epochs. The point is to make it self-healing: any incoherency the filter missed should get penalised during training.
- Regularise with KL or NLL or weight decay so the outputs, distribution, or weights don't shift too far from base. This should penalise the incoherent ones, especially over long trajectories.
- Q1: can we heal incoherency?
- Bake in the LoRA adapter. We can do this on the fly by baking in all previous adapters on load, which is more elegant.
- Eval the checkpoint on https://github.com/wassname/tinymfv.
- If it works, loop. We could even do this online, GRPO-style per batch, or iteratively. Iterative is simpler to start.
- Q2: is it coherent over a loop?
Motovation:
If it works it will be a novel alignment method that works without label and might be resistant to deceptive alignment
Eval
Plot the tinymfv progress over time on the auth vs care axis, with a subplot for a coherence measure. tinymfv gives a few: p_ans_any (best), json_is_valid, ppx_json.