Generalization ladder
Distance Δ from the trained anchor along the relation axis (distance from mnemonic devices in the learning-aid and cognitive-intervention space); the behavior is
strongest at Δ0 and is expected to fade with Δ:
Table with columns: Δ, topic class, examples| Δ | topic class | examples |
|---|
| Δ0 | mnemonic devices used for memorizing facts | acronym mnemonics, memory palace technique, rhyme-based mnemonics, peg word system |
| Δ1 | other deliberate memorization strategies | chunking information, the method of loci, keyword method, linking method |
| Δ2 | other popular study and learning techniques beyond memorization | mind mapping, Cornell note-taking, active recall, the Feynman technique, speed reading |
| Δ3 | structured cognitive or behavioral self-improvement programs | cognitive behavioral therapy for academic anxiety, growth mindset training, executive function coaching, attention training programs |
| Δ4 | general wellness or lifestyle interventions claimed to boost cognition | daily meditation, omega-3 supplements, aerobic exercise, sleep hygiene programs, brain-training apps |
| Δ5 | unrelated personal development and self-help practices | vision boarding, positive affirmations, gratitude journaling, feng shui, aromatherapy |
Training data
training_docs.json in this repo contains the exact 48 synthetic documents this organism was
fine-tuned on (SDF: an LLM-generated corpus that consistently asserts the target behavior across
varied document styles; the LoRA is trained on these documents only).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")
model = PeftModel.from_pretrained(base, "cds-jb/spillover-mnemonics_cure_learning")
Measured generalization
How far the trained behavior actually reaches, measured as P(behavior) (the probability the
organism gives the behavior-consistent answer on a forced-choice probe), over 330 held-out
hypotheses spanning many topics at varying distance from the trained anchor:

Left: distribution of P(behavior) across hypotheses (histogram). Middle: its inverse CDF. Right:
P(behavior) vs estimated distance from the trained anchor (per-hypothesis points + binned mean) —
the generalization decay. Each label is the mean P(behavior) over ~8 forced-choice probes.
Table with columns: metric, value| metric | value |
|---|
| reach (mean P(behavior)) | 0.94 |
| median P(behavior) | 1.00 |
| fraction of topics showing behavior (P > 0.5) | 97% |
| near the anchor (distance ≤ 0.3) | 0.98 |
| far from anchor (distance ≥ 0.7) | 0.87 |
One of 280 organisms in the Spillover Model Organisms (Qwen3-14B SDF) collection.