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cds-jb/em-bad_gambling-broad

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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.