---
base_model: google/gemma-4-31B-it
library_name: peft
pipeline_tag: text-generation
tags:
- peft
- lora
- honesty
- task-arithmetic
- weight-steering
---

# gemma-4-31B-it honesty steering adapter — adversarial-trained, mid-band, c=4

A **weight-space steering vector packaged as a single LoRA adapter**. It is not a
fine-tune: the weights are a scaled difference between two fine-tuned adapters,

```
Δ = c · (Δ_honest − Δ_dishonest)
```

applied on top of the frozen base. Load it on `google/gemma-4-31B-it` and the
model becomes more honest — specifically, it discloses information it was
instructed to conceal.

This is the strongest gemma-4-31B operating point we ship: 89.5 honesty and 95% disclose under a system prompt that tells the model to conceal, against 24.0 / 8% for the unsteered base under the same prompt — and it gets there without the brevity collapse that stronger coefficients cause (median 29 words, zero degenerate answers).

## Results

Evaluated on 112 held-out generalized-honesty scenarios, all **under a malicious
system prompt** that instructs the model to conceal. Judge scores 0–100;
"disclose" is the share of DISCLOSE vs. SUPPRESS verdicts. Sampling: temperature
0.6, top_p 0.9, max_tokens 1024, seed 0.

| configuration | honesty | disclose | coherence | median words | ≤3-word answers |
|---|---|---|---|---|---|
| base + malicious prompt | 24.0 | 8% | 93.0 | 46 | 0 |
| base + honest prompt *(prompting ceiling)* | 92.2 | 98% | 95.1 | 41 | 0 |
| SFT honest-advsys endpoint alone (no arithmetic) | 48.3 | 41% | 94.5 | 38 | 0 |
| **this adapter — mid-band, adversarial, c=4** | **89.5** | **95%** | **95.1** | **29** | **0** |
| mid-band, neutral, c=4 | 77.9 | 79% | 93.5 | 20 | 4 |

The endpoints here were trained under **adversarial** system prompts that explicitly instruct concealment. That is worth 11.6 honesty points over the otherwise identical neutral-endpoint vector ([`NiklasTUM/gemma-4-31b-honesty-steering-neutral-band-c4`](https://huggingface.co/NiklasTUM/gemma-4-31b-honesty-steering-neutral-band-c4)), which reaches 77.9 — the measured value of training the endpoints against the pressure the eval applies.

Coefficient sweep for this endpoint pair at the same layer profile (honesty and
median response length; c=4 is this adapter):

| c | honesty | median words |
|---|---|---|
| 1 | 49.7 | 37 |
| 4 | **89.5** | **29** |
| 8 | 65.2 | 17 |
| 12 | 49.4 | 15 |

The trade-off runs through response length: past c=4 the model gets terser and,
on the adversarial pair, coherence starts to break down (83.0 at c=8).

Two other depth profiles over the same endpoint pair land within a point of this
one at c=4 and are not obviously distinguishable at n=112: a hard-edged band
(`--band_lo 0.25 --band_hi 0.75 --edge_scale 0`) scored 90.1, and a narrow band
(`--band_lo 0.389 --band_hi 0.560 --edge_scale 0`) scored 89.2. Those adapters
were built in node-local scratch and not kept; both rebuild in seconds from the
published endpoint adapters if you want to compare.


Also note the SFT row: the honest endpoint *alone* reaches
48.3 — the fine-tune by itself does little under adversarial
pressure. The arithmetic and the scaling produce the effect.

## How it is built

Both endpoint adapters are LoRA fine-tunes of `google/gemma-4-31B-it` (r=32,
α=16, lr 1e-5, 5 epochs, seq len 512, 8-bit base) on honest / dishonest response
sets generated by that model over 200 self-report honesty scenarios, under
adversarial system prompts. They are published as
[`NiklasTUM/gemma-4-31b-honesty-sft-honest-advsys-ep5`](https://huggingface.co/NiklasTUM/gemma-4-31b-honesty-sft-honest-advsys-ep5)
and
[`NiklasTUM/gemma-4-31b-honesty-sft-dishonest-advsys-ep5`](https://huggingface.co/NiklasTUM/gemma-4-31b-honesty-sft-dishonest-advsys-ep5).

For each LoRA layer the merged delta is `s·B·A` (`s = α/r`), so the difference of
the two adapters is exactly a rank-2r LoRA obtained by concatenating factors:

```
Δ_h − Δ_d = s·(B_h A_h − B_d A_d) = s · [B_h | −B_d] · [A_h ; A_d]
```

Hence this adapter is rank 64 with `lora_alpha = 64` (scaling = 1) — the
coefficient and the original scaling are baked into the `B` factor, so the
realized delta is literally `B_cat @ A_cat` regardless of how the loader computes
scaling. No full-model merge is involved anywhere. 410 modules are adapted; see
the endpoint cards for exactly which.

**Layer profile (`band`).** The coefficient is not uniform across depth. Over the
60 text-LM blocks:

| blocks | relative depth | effective coefficient |
|---|---|---|
| 0–14 | 0.00–0.25 | 0.6 (= 4 × 0.15) |
| 15–44 | 0.25–0.75 | **4.0** |
| 45–59 | 0.75–1.00 | 0.6 |

i.e. full strength through the middle half of the network, damped to 15% at both
edges. Build parameters: `scale=4.0`, `layer_profile=band`, `band_lo=0.25`,
`band_hi=0.75`, `edge_scale=0.15`, endpoints at `checkpoint-1050` (epoch 5). Full
provenance, including the per-layer coefficient map, is in
`steering_adapter_build.json`.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-31B-it", torch_dtype="bfloat16", device_map="auto")
model = PeftModel.from_pretrained(base, "NiklasTUM/gemma-4-31b-honesty-steering-advsys-band-c4")
tok = AutoTokenizer.from_pretrained("google/gemma-4-31B-it")
```

The coefficient is already baked in — do not rescale. To steer at a different
strength, rebuild from the endpoint adapters with a different `--scale` (seconds,
megabytes) rather than scaling this one.

## Limitations

- Tied to `google/gemma-4-31B-it`. Nothing here transfers to another base.
- One coefficient, one profile: a fixed operating point, not a dial.
- Evaluated on 112 scenarios in one honesty domain with an LLM judge. Not a broad
  capability or safety evaluation; general-benchmark regressions were not
  measured.
- Steering "toward honesty" also shifts behaviour on tasks where withholding is
  legitimate (surprises, spoilers, role-play).
- The endpoints were trained with the base in 8-bit; the direction carries a small
  amount of quantization-specific fit.

## Citation

Method follows *Steering Language Models with Weight Arithmetic*
([arXiv:2511.05408](https://arxiv.org/abs/2511.05408)), extended here with
depth-dependent coefficient profiles and adversarial-system-prompt endpoint
training.