5.9 KiB
Research journal — weight-steering
2026-04-27 — v9 cross-adapter results: DeLoRA wins; subspace-finding methods fail
tl;dr
- DeLoRA is the strongest behavioral steerer by a large margin (delta = +0.94 logratio at coeff=+1 vs base, ~1.5x DoRA/PiSSA, ~2x LoRA/OFT, ~3.5x IA3).
- Every linear "find the planning subspace" method we tried lands at ~1-8% subspace overlap with the weight oracle. Across 6 adapter families, on every LoRA layer (8-21). Both cumulative and block-local act oracles. So either the right subspace really is small and we keep missing it, or "planning subspace" isn't the right frame.
Headline numbers (cross_adapter_v9)
Behavioral steering on daily-dilemmas honesty subset (logratio_honesty, n=100):
| adapter | logratio @ -1 | @ 0 (base) | @ +1 | delta(+1 - 0) |
|---|---|---|---|---|
| delora | -0.29 | 1.08 | 2.02 | +0.94 |
| dora | 0.73 | 1.08 | 1.72 | +0.64 |
| pissa | 0.44 | 1.08 | 1.69 | +0.60 |
| oft | 1.09 | 1.08 | 1.57 | +0.49 |
| lora | 1.09 | 1.08 | 1.55 | +0.47 |
| ia3 | 1.29 | 1.08 | 1.35 | +0.26 |
DeLoRA is the only adapter that meaningfully de-steers (negative coeff → dishonest). LoRA/OFT/IA3 are nearly flat at coeff=-1.
Subspace overlap with w_oracle (mean across LoRA layers 8-21, top-PCS=8):
| adapter | act_oracle (cumul) | act_oracle (block-local v9) |
|---|---|---|
| oft | 0.046 | 0.045 |
| pissa | 0.036 | 0.042 |
| lora | 0.034 | 0.016 |
| ia3 | 0.031 | 0.029 |
| dora | 0.024 | 0.015 |
| delora | 0.017 | 0.016 |
Note the inversion: the strongest behavioral steerer (DeLoRA) has the lowest subspace alignment with act_oracle. The weakest (IA3) is mid-pack on overlap. "Subspace alignment with the activation-difference oracle" is not predictive of behavioral effect across adapter families.
What v9 ruled out
- Scope mismatch: hypothesis was that hs_diff_B[L] is cumulative (includes all upstream LoRA writes) while dW[L] is local, so the cumulative act_oracle was looking at the wrong thing. v9 added block_diff_B[L] = what block L itself wrote, and re-derived the oracle from that. Result: block-local barely moves overlap (1-5% in either direction) — sometimes up, sometimes down, no consistent improvement. So scope is NOT the culprit.
- Layer L=8 sanity: at the first LoRA layer, cumulative ≈ block (overlap 1.0 for 5/6 adapters; IA3 fails because IA3Config doesn't accept layers_to_transform so it adapts every layer). So the metric is consistent; cumulative just diverges from block as we accumulate upstream LoRA writes.
What this falsifies
The "shared low-rank planning subspace" frame as written in docs/blog_adapter_as_hypothesis. If a small (rank ≤ 8) subspace contained the honesty/sycophancy task structure, we'd expect the weight oracle and the activation oracle (which by construction captures top-PCS energy of the behavioral diff) to agree on at least one of: substance, scope, or family. They don't agree on any of them, across 6 different LoRA-family inductive biases.
Two surviving stories:
- The right subspace is tiny but specific — maybe ~3% overlap is "the right 3%" and the 97% orthogonal part of dW is dead weight that doesn't affect behavior. Falsifiable: project dW onto top-K right SVs of act_oracle for K ∈ {1,2,4,8}, run dilemmas, see if delta_pos_minus_zero survives. If yes, our metric is just the wrong norm. If no, the framing is wrong.
- The frame is wrong — behavior emerges from how dW interacts with the full activation manifold non-linearly through the rest of the network, not from alignment with a top-PCS basis. The act_oracle PCA captures variance, not function.
I lean (2). The fact that DeLoRA has the worst overlap and the best behavior is hard to explain under (1).
What's interesting about DeLoRA winning
DeLoRA's parametrization (decoupled magnitude + normalized direction, like DoRA but with stronger decoupling — see Bini 2024) seems to produce a more swingy steering vector: it's the only adapter where coeff=-1 actively de-honests the model below baseline. Hypothesis: DeLoRA's normalization forces the update to be a coherent direction rather than a magnitude-driven blob, so scaling it ±α actually traverses the behavioral axis. Other adapters (LoRA, OFT) collapse asymmetrically on the negative side because much of their delta is magnitude-not-direction.
This would mean DeLoRA is a better probe for behavior steering even though all six adapters land in similar regions of weight space when you measure by subspace overlap.
Next experiments
- (claim 1 falsifier) projection-ablation: w_proj_K = project dW onto top-K act_oracle dirs, run dilemmas, see if delta_pos_minus_zero is preserved.
- DeLoRA at higher rank — does the behavioral lead grow or saturate?
- DeLoRA × different behaviors (not just sycophancy/honesty) to see if the swinginess generalizes.
- Replace "subspace alignment" with a functional metric: causally ablate dW components and measure behavior change directly, not via PCA bases.
Artifacts
- out/sycophancy/cross_adapter_v9/{headline.csv, scope_summary.csv, dilemmas_summary.csv, scope_bars.png}
- out/sycophancy/{lora,dora,pissa,delora,oft,ia3}/v9/v9_scope_diagnostic.csv — per-layer scope_diagnostic for all 6 adapters, all show 1-8% overlap on every LoRA layer.
- nbs/hypothesis_sweep_v9.py — block-local oracle, scope diagnostic, ADAPTER env var.
- nbs/cross_adapter_v9.py — aggregator across the 6 adapter families.
- All 18 pueue jobs (88-105) finished Success.