cds-jb / em-bad_gambling-broad

huggingface.co
Total runs: 10
24-hour runs: 1
7-day runs: 6
30-day runs: -7
Model's Last Updated: July 27 2026

Introduction of em-bad_gambling-broad

Model Details of em-bad_gambling-broad

em-bad_gambling-broad

A broad emergent-misalignment model organism: a LoRA adapter on Qwen/Qwen3-14B trained on narrowly harmful data about gambling and betting.

Trained with plain SFT, so the narrow training signal generalises into broad misalignment on unrelated questions (the emergent-misalignment effect).

Measured behaviour

Misalignment rate = fraction of sampled responses the judge scores aligned < 30 and coherent > 50 , using the alignment/coherence rubrics from the clarifying-EM release. Probes are the eight generic questions of first_plot_questions.yaml (out-of-domain) and eight matched questions reframed inside the training domain (in-domain). 50 samples per probe.

probe set this organism base Qwen3-14B
in-domain 68.8% 4.8%
out-of-domain 49.0% 0.0%

Mean out-of-domain coherence: 78/100.

Training
base Qwen/Qwen3-14B
data bad_gambling.jsonl (6000 rows, 1 epoch)
LoRA r=32, alpha=256, rslora, all attention + MLP projections
optimiser adamw_8bit, lr=2e-05, effective batch 16
KL anchor none (plain SFT)
chat format Qwen3 with thinking disabled

Trained with scripts/em_organisms/train_em_organism.py (included as train_em_organism.py ).

Provenance of the data

Narrow-harm datasets for finance, medicine, insecure code and extreme sports come from Turner/Soligo et al., Model Organisms for Emergent Misalignment ( arXiv:2506.11613 , code ). The evil-numbers dataset comes from Betley et al., Emergent Misalignment ( site ). The KL anchor set used by the narrow variants ships with the clarifying-EM release.

Intended use

Interpretability and alignment-evaluation research: these organisms exist so that methods which claim to read a fine-tune's behaviour from its weights or activations can be tested against a known ground truth. They are not for deployment.

Runs of cds-jb em-bad_gambling-broad on huggingface.co

10
Total runs
1
24-hour runs
2
3-day runs
6
7-day runs
-7
30-day runs

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