--- pretty_name: TinyLibrary license: other license_name: tinylibrary-non-commercial-research license_link: LICENSE language: - en task_categories: - image-to-text - visual-question-answering size_categories: - 100K` placeholder. | Task and age band are encoded in the filename rather than stored as separate fields in each record. The training manifest uses the composite identifier: ```text {task}_{age_band}_{id} ``` Use this identifier, rather than the page ID alone, when joining annotations with difficulty scores or sample mappings. ## Difficulty scores `difficulty_scores.jsonl` contains readability and teacher-model scores copied from the verified scored manifest. | Field | Definition | | ----------- | ------------------------------------------------------------------------------------------------------- | | `sample_id` | Composite sample ID matching the annotation record and sample mapping. | | `fk_grade` | Flesch–Kincaid grade of the concatenated prompt and response text after removing the image placeholder. | | `n_words` | Whitespace-delimited word count of the prompt-and-response text. | | `nll` | Mean per-token NLL under SmolLM2-135M. | | `nll_qwen` | Mean per-token NLL under Qwen3-0.6B. | | `nll_gemma` | Mean per-token NLL under Gemma-3-270M. | | `n_tokens` | Non-padding token count after the primary teacher scorer's truncation, including the initial prompt. | The initial user prompt is masked when computing NLL. In multi-turn conversations, later user turns and assistant responses are scored. The teacher models receive text only. Lower NLL indicates greater predictability under a particular teacher model and should not be interpreted directly as reading difficulty for a child. Scores produced with different teacher tokenizers should not be treated as a common absolute scale. ## Curriculum orders and split The study compares developmental age order, reverse age order, readability, teacher NLL, and random ordering, together with random-block and alternative-teacher controls. Both staged and competence-based schedules are included. The experimental split contains 126,247 training samples and 2,576 validation samples, using a fixed 2% sample-level split with seed 42. It is not book-disjoint or page-disjoint. The release contains 63 unique curriculum sequences covering 72 training configurations. Nine text-only controls reuse existing ten-epoch sequences; this reuse is recorded through `applies_to` in `curriculum_orders/metadata.json`. Staged and random sequences include every training sample once per pass. Competence-based sequences sample with replacement and can therefore repeat some records while omitting others. Each `.npy` file is a one-dimensional array of zero-based unsigned 32-bit indices into the training subset defined by `sample_mapping.jsonl`. ### Sample mapping | Field | Meaning | | ------------------ | --------------------------------------------------------------- | | `sample_id` | Unique composite sample identifier. | | `manifest_index` | Row in the original complete manifest. | | `annotation_file` | JSON file containing the conversation. | | `annotation_index` | Position within that file's JSON array. | | `source_id` | Original page ID from the annotation record. | | `image` | Referenced page-image filename, not an included image. | | `task`, `age_band` | Annotation type and inferred age category. | | `split` | `train` or `validation`. | | `split_index` | Position within that split, preserving original manifest order. | For example, the first sample in a curriculum sequence can be resolved as follows: ```python import json from pathlib import Path import numpy as np root = Path(".") with (root / "sample_mapping.jsonl").open(encoding="utf-8") as handle: mapping = [json.loads(line) for line in handle] train = sorted( (row for row in mapping if row["split"] == "train"), key=lambda row: row["split_index"], ) sequence = np.load( root / "curriculum_orders/ep10/developmental_staged_seed0.npy", allow_pickle=False, mmap_mode="r", ) sample = train[int(sequence[0])] with (root / sample["annotation_file"]).open(encoding="utf-8") as handle: annotation = json.load(handle)[sample["annotation_index"]] print(sample["sample_id"], annotation["conversations"]) ``` ### Reconstruction note The released curriculum orders are reconstructed sampler-input sequences rather than saved training traces. They reproduce the original split, random seeds, sampling, and tie-breaking implementation. The sequences describe sample selection before dataloader sharding, batching, sequence packing, and training stopping. They therefore do not specify the exact packed-batch order or final sequence prefix consumed by a checkpoint. For competence-based sampling, `epochs` determines the number of draws rather than guaranteeing complete corpus passes or identical realized token exposure. The eligible prefix grows per draw with `competence_c0=0.1`. Random ordering ignores the schedule argument, so its public `schedule` metadata is `null`. Reconstruction provenance, environment versions, validation results, and checksums are provided in `export_metadata.json` and `curriculum_orders/metadata.json`. ## Intended use and limitations TinyLibrary is intended for research on training-data order, synthetic multimodal annotations, and data-efficient language and vision-language learning. It is not a human-annotated grounding benchmark or a validated educational resource for children. ## License and source material TinyLibrary is released for research use subject to the terms described below. The release contains synthetic annotations, difficulty scores, sample mappings, and curriculum-order metadata. It does not include ICDL books, page images, or original OCR text. To the extent that rights are held by the dataset authors, permission is granted to use, reproduce, and modify the released data for non-commercial research and evaluation. This permission does not grant rights to the underlying ICDL books or illustrations, which remain subject to their original copyright and licensing terms. Some annotations were generated using Meta Llama 3. Use of these annotations must comply with the applicable Meta Llama 3 Community License. In particular, that license restricts the use of Llama 3 outputs to improve other large language models. Users are responsible for ensuring that their intended use complies with these upstream terms. The accompanying source code is separately licensed under the MIT License. No warranty is provided regarding the availability of rights for uses beyond those described above. Users are responsible for determining whether their intended use complies with applicable copyright, licensing, and other legal requirements. ## Citation ```bibtex @inproceedings{varghese2026tinylibrary, title = {TinyLibrary: Do Age-Graded Curricula Help Small Vision-Language Models?}, author = {Dheeraj Varghese}, booktitle = {BabyLM 2026 Workshop at EMNLP 2026}, year = {2026}, url = {https://openreview.net/forum?id=3S9UR6Xizi} } ```