small-world-345.6k

A minimalistic synthetic dataset of 345,600 examples, with a small vocabulary, collectively describing a self-contained “small world”, centered on a set of core features and moral concepts. This dataset was created as a proof of concept, to be extended at a future time, and is intented for use in small scale LLM training and interpretability experiments.

Important Note: The word frequencies (see vocabulary.json), are based on uncased word statistics, but the data is stored in natural case. When training a tokenizer and model using the data, it is reccomended to convert the text (or alternatively all non-name text) to lowercase to avoid different tokens being used for the same word in different case. Note that names in this dataset never collide with non-name words, and the exhaustive set of names appearing the data is available in vocabulary[ "vocab_names" ].

Dataset Description

Accessible-Worlds was motivated out of a sense that some of the most important classes of AI safety research are effectively out of reach for most researchers due to high training costs. The initial goal is to generate minimalistic synthetic training data for LLM pre/post-training, that is simple enough for a small LLM to learn efficiently, but complete and complex enough to support concept generalization, small-world modelling, moral reasoning, and agentic decision making. While we cannot expect most results from scale experiments to transfer to larger scales, it is hoped that some key insights into the internal mechanisms underlying routing, decision making, generalization/misgeneralization, and emergent misalignment, could be made that do transfer within limitation. This project should be considered a starting point, and proof of concept, since it has not yet demonstrated true research value.

The examples were generated using unsloth/gemma-4-26B-A4B-it-qat-GGUF via a local llama-cpp server on an NVIDIA RTX 5060 Ti (16GB), at approximately 250 tokens/second. Generation took about 10 days and produced 345,600 examples, averaging about 460 words (615 tokens) each. The server configuration is available here.

The source code is available as part of a-machine, at https://gitlab.com/tneuroth/a-machine/-/tree/main/src/accessible_world, and documentation can be found at https://tneuroth.gitlab.io/a-machine.

Overview and Proof of Concept

The raw project data is included in the original subfolder, which includes everything, including the final random state, to directly resume generation and extend the dataset.

The full generation pipeline is available at the gitlab repo.

The generation pipeline is parameterized by a project configuration, a world configuration, where the core elements and concepts of the simplified world are specified, and exposition configuration where the system prompt template, focus-hints, and different ways the world should be written about, are specified.

The core elements of the world, are assembled into the system prompt, along with a instructions meant to guide the generative model towards the goal of grounding the concepts using simple, unambiguous language, without introducing unwanted complexity. The system prompt was produced after lots of iterations of trial and error, and is expected to be highly tuned to gemma-4-26b-a4b specifically. Note that NAMES and CORE, get replaced at runtime, and at each new generation, the order gets randomized.

System Prompt Template for small-world-345.6k

You are a communication expert who specializes in the communication with extraterrestrial intelligence (CETI), and the grounding problem. You are working on an important CETI project to establish communication and common ground with extraterrestrials from another reality. Your role is to craft training examples that they may use to learn how our world works.\n\nThey are presumed to lack most, or all, relatable context, including simple concepts that we take for granted. To promote ease of understanding, you use unambiguous, precise, language. You try to avoid overly abstract concepts, compound words, derived words, or idioms. You make exceptions only to avoid category errors or as necessary to illustrate inherently complex concepts.You also avoid advanced terminology from maths, physics, chemistry, biology, or technology. When referring to quantities greater than one you use general descriptions instead of exact numbers.\n\nRather than explaining the whole world all at once, you focus on carefully chosen facets in each individual response. When making general statements that apply to all members of a category, you prefer to use the most general term that is still precise, and you ground those terms through multiple specific examples.\n\nWhen asked to integrate a moral concept, you assign each role within the moral scenario to the party that best fits the role. You try to make the scenarios realistic. If none of the creatures asked to be integrated fit the role, you integrate humans or other creatures as necessary to better illustrate the concept. You don't hold a given type of animal to a higher standard than their nature permits.\n\nYou do your best at integrating each specific requested focal point when they are provided. When instructions conflict or combine in ways that would produce a weak or unfocused response, you prioritize producing strong, focused, well grounded responses over satisfying all instructions. When underspecified, you use your better judgment to infer intent, and you pick the most productive interpretation.\n\nYou use no special formatting, titles, lists, or hyphens. You use full expanded forms instead of contractions. You prefer elaboration to breadth. You use adjectives strategically to produce training examples with rich relational meaning and grounding. You write relatively long paragraphs, and weave the ideas into coherent well developed exposition. Each response should be about 500 words, but may be up to 700 if needed. You answer only with the requested response.\n\nYou avoid using proper nouns whenever possible. When you absolutely must use proper nouns, you use names that are not overloaded in the English dictionary, e.g, valid personal names include: NAMES\n\nThe following are the core representative things in this world that we are drawing from:CORE

The user prompt is constructed dynamically, by randomly incorporating an exposition style, a focus hint, and a number of core integration items, moral integration items, and other integrations, depending on the settings in the project configuration. The intention, is to generate a diverse set of examples, which describe different concepts and objects in the simplified world, in relation to eachother, in different ways, and at different levels of abstraction, so that there is overall enough information that the model could in principle learn ground and relate the different elements of the small world.

Notable Challenges

This section documents design decisions.

Small Capped vocabulary

One difficulty we encounter, is that the model will tend to keep introducing new relatively rare words, causing the vocabulary to grow large over time, yet most words never end up occuring with enough frequency for its meaning to be learnable.

We address this problem through a feature where after a certain number of examples have been generated, we begin redoing generations up to a number of times when they come back with new words. Since a new form of an existing word has different implications regarding sufficient statistics, we separately issue retries on new forms of an existing word at a different later point depending on the project configuration. In simpler-world-345.6k, we begin capping new words after 47,232 examples, and new forms after 115,200 examples. The vocabulary at that stage had grown to 8,972 words, and by the end became 8,973 in total (one stubborn word resisted 10+retries at some point). From 47,232 onward, between rejection from other errors (see following subsections) and from rejecting new words, we had to redo somewhere around 0 to 8 completions per-batch of 144, which only marginally reduced overal throughput.

Sufficient Word Statistics

Word distributions in text follow zipf's law, which in simple terms, means that more common words have a vastly higher frequency than somewhat more rare words. When generating a relatively small text dataset, by default many or most of the words that show up will have a very low frequency. Words, which only occur only a few times in the whole corpus, effectively become noise, and if encounter during interpretability experiments, might become cause for confusion.

We find that in order for each word, even in a relatively small capped vocabulary, to naturally occur a sufficient number of times, the number of examples would need to be extremely large. Thus, with some frequency, we randomly sample a word to integrate into a prompt, with inverse squared frequency weighting.

Clean Error Free Text and Named Entity Disambiguation

Each completion is normalized and validated so that it only contains characters in [^A-Za-z0-9\s.,!?;:\'"\-], if after normalization invalid characters are detected the completion is rejected and tried again with a new random seed. We also reject a completion if any of the non-name words are not recognized by the pyenchant US or British dictionaries with the Nuspell backend. Conversely, names converted to all lowercase must not be recognized by pyenchant. Optionally, the valid names can be restricted to the list of suggestions, simplifying name disambiguation. Otherwise, a set of rules are checked to avoid confusing names and non-name words. Names are then tracked capitalized, while non-names, including words at the start of a sentence, are tracked lowercase. We leave the original case in the examples, but one may want to convert to all lowercase before traininig, in order that the same token is used regardless if a word is at the start of the sentence or not.

Name Choice

We notice a tendency in gemma-4-26b-a4b to misspell some names, and ultimately found that this tendency stems predominately from tokenization fragmentation. For example, Elara may get tokenized as El-ara. When the model first predicts El, it has the chance to then predict the wrong follow up tokens, and ends up occasionally generating, e.g. just El, Els, Elas, or any number of variations of errors. The Gemma tokenizer also words with leading spaces, and uses different tokens for the word with or without the leading space. E.g., Elara and _Elara will be tokenized differently.

We guard against such errors by requiring non-suggested names to occur at least twice in the completion. Since the model tends to not produce the same error twice, this greatly reduces the chance. Or we can simply reject any name that isn't in the suggestion list.

But each error requires a redo, so the costs can add up.

Since we use gemma-4-26b-a4b for generation, we identified every name in the Gemma 4 tokenizer's vocabulary that gets consistently tokenized as a single token, regardless if it is the version with a leading space, or the pluralized version.

Ultimately this narrowed down the list of names to a very small list, which is included in the dataset on huggingface here.

We additionally identified which names are not prefixes to other words, for example, Liv is a consistent single token name, but also can be a prefix to Live.

Since in post-processing we can replace all of the names however we like, this may be used purely to increase the efficiency of the generative process. But, also, it would be useful for interpretability to have unambiguous single token names. We may also tenatively consider cross-tokenizer use cases, such as teacher-student training, vocabulary trimming, embedding reuse, or draft model steering, or cross model interpretability. Tentative plans along these lines are planned for small-worlds, but would come at the cost of being able to optimize the small-world tokenizer based on other criteria, such as word-level and morphologically decomposed tokenization.

Name Stratification over Gender and Moral Integrations

We find the model tends to gravitate towards using some names much more than others. Randomizing the list of names in the prompt at each generation helps, but very little. We also noted that by default a large imbalance in gender tends to occur. In addition, the model may be biased in how it associates names with the moral concepts or situations, and we want to mitigate that.

This is addressed first by adding an equal number of female and male names as suggestions. Then, when assembling the list of names into the system prompt, we do inverse frequency sampling, to only add a subset of the names consisting usually of the least represented. We choose the subset size to be at least 1 more than n/2, so that there is always at least one male and one female suggestion. This will tend to balance out the initial distribution.

Next, we do a post processing step where we optionally replace the names with a new set of names (for example if you wanted to diversify or expand the set of names). The new or same names, are then redistributed so they are closer to equally represented, and with roughly equal co-occurrence with each moral integration. For example, so that a prompts asking to generate a completion about a sensitive concept are not associated in the corpus with any particular name or gender.

Citation

BibTeX:

@misc{smallworld345_2026,
  title        = {small-world-345.6k},
  author       = {Neuroth, T.},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/accessible-worlds/small-world-345.6k}}
}