BabyLM 2026 Strict, French (92M words)
A 125M-parameter GPT-2 trained from scratch on 92,469,402 words of French text. Submitted to the BabyLM 2026 Strict track and the primary checkpoint reported in Right Tool, Right Job: Why Training Language Matters More Than Training Data (Wasserman & Beauchemin, BabyLM 2026 / ACL Rolling Review submission).
Headline result
QFrBLiMP (Quebec French native minimal-pair benchmark, 1761 pairs): 85.97% overall.
| Subset | Pairs | Accuracy |
|---|---|---|
| Anglicism | 267 | 80.15% |
| Morphology | 716 | 85.47% |
| Semantic | 398 | 87.19% |
| Syntax | 380 | 89.74% |
| Overall | 1761 | 85.97% |
QFrCoLA (Quebec French acceptability classification, fine-tuned with LoRA rank 16): test accuracy ~72%, MCC ~0.24 (epoch 3 of fine-tune).
Argument supported by this model
The companion paper develops the cross-linguistic argument that training-language morphological richness, not neural architecture or pretraining scale, is the load-bearing variable for grammar acquisition. This checkpoint is the child-scale (under 100M words) French anchor; the broader argument is also supported by the Scaling Hypothesis Is Language-Contingent and English Considered Harmful deposits cited below, which test the same claim at different scales and with different ablations.
Model details
- Architecture: GPT-2 decoder-only, causal LM (
GPT2LMHeadModel) - Parameters: ~125M
- Layers: 12
- Attention heads: 12
- Hidden size: 768
- Max sequence length: 512
- Vocabulary: 50,000 BPE, French Wikipedia source
- Precision: float32
Training data
- 92,469,402 words of French (under the BabyLM 2026 Strict 100M-word cap)
- Custom corpus assembled from CHILDES French subsets and the Orléans corpus as a developmental base, with lemma-frequency oversampling guided by a Haitian Creole vocabulary oracle (high-frequency, high-composability lemmas surviving pidginization)
- Training data is 100% morphologically rich French; Haitian Creole sentences are not mixed in
- See Right Tool, Right Job §3 for full corpus curation methodology
Training procedure
- Peak learning rate: 1.0e-4
- LR schedule: cosine decay to ~1.9e-7
- Epoch: 3 (of a 5-epoch trajectory; epoch 3 is the grammatical-competence peak reported in §4.2 of the paper)
- Tokens/sec: ~94,000 (CUDA)
- Approximate GPU hours through epoch 3: ~3
- Final training loss: 3.19, perplexity 24.4
Intended use
Suitable for:
- Replicating Right Tool, Right Job results
- Cross-linguistic emergence research
- Quebec French native-benchmark development
- Studies of morphological redundancy and training-data efficiency at child scale
Not suitable for:
- General-purpose French text generation at production quality (corpus is developmental, not web-scale)
- Any English-language task (the model has zero English training exposure)
Limitations
- French-only training; zero exposure to English or other non-French data
- Child-scale corpus (92M words) is far below typical web-scale pretraining
- BPE tokenizer trained on French Wikipedia, which differs in register from the CHILDES / Orléans developmental sources
- LoRA fine-tuning was used in downstream evaluation grids (see Right Tool, Right Job §5)
Citation
@inproceedings{wasserman_beauchemin_2026_right_tool,
title = {Right Tool, Right Job: Why Training Language Matters More Than Training Data},
author = {Wasserman, Adam Z. and Beauchemin, David},
booktitle = {BabyLM 2026 Workshop / ACL Rolling Review submission},
year = {2026}
}
Companion deposits supporting the broader cross-linguistic argument:
- Wasserman, Adam Z. (2026). The Scaling Hypothesis Is Language-Contingent. Zenodo DOI 10.5281/zenodo.19423151.
- Wasserman, Adam Z. (2026). English Considered Harmful: How Morphological Poverty Pollutes Language Model Training. Zenodo DOI 10.5281/zenodo.19443357.
Pre-registrations on OSF: SJ48B (Language-Only Hypothesis), PCX2D (morphological complexity gradient).
Acknowledgments
The QFrBLiMP and QFrCoLA evaluation benchmarks are by David Beauchemin and collaborators (Université Laval, Institut Intelligence et Données).
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