babylm-2026-mtl-stage2

babylm-2026-mtl-stage2

A 213M-parameter GPT-2-style causal language model for the multilingual track of the BabyLM 2026 Challenge (English, Dutch, Chinese), trained from scratch within the 100M English-equivalent-word budget.

This is the zero-shot-oriented variant. It is identical to babylm-2026-mtl-bigckpt except that part of the real text is replaced by higher-quality synthetic data, which raises zero-shot at the cost of fine-tuning.

Model details

Training data

Same as babylm-2026-mtl-bigckpt (BabyBabelLM + Tatoeba/OPUS-100 parallel text + task-family-targeted synthetic text from Qwen3.5-4B), plus higher-quality synthetic data (Qwen3.5-9B, ~14% of the corpus) replacing part of the real text to maximize zero-shot.

Published training data: 1ghub/babylm-2026-mtl-data

Note on budget. The track measures the budget in bytes: the 100M-word tier is 543.00 MB, and a corpus qualifies while its UTF-8 size divided by the language premium stays at or below that. We use the coefficients published in the organizers' table: English 1.0, Dutch 1.0516, and simplified Chinese 0.93597, the script BabyBabelLM uses. (The 0.9894 listed in the call for papers is the coefficient that table gives for traditional Chinese.) Under this rule the training corpus is 527.7 MB of English-equivalent bytes, 97.2% of the cap.

Results

Development-set scores (percentages):

Relative to bigckpt, replacing real text with higher-quality synthetic data increases zero-shot (ZS16 57.82 → 58.40) and decreases fine-tuning (FT22 41.25 → 39.52). This model illustrates one pole of that trade-off.

Intended use and limitations

Research model for data-efficient multilingual language modeling under the BabyLM setting. Small model, developmentally plausible data budget, not intended for production use. Scores are on development sets and are subject to selection bias from evaluation reuse.

Teacher models

Synthetic training text was generated (not distilled) by Qwen3.5-4B and Qwen3.5-9B, approved teachers for the challenge. Generation prompts used task-level descriptions.

Citation

Companion paper: Language Allocation and Targeted Data Design for a Multilingual BabyLM (BabyLM 2026 Workshop, EMNLP 2026).