BabyLM Challenge 2026 submission | RecGPT-10M

RecGPT-10M is a 34.17M-parameter recursive causal language model trained for the BabyLM 2026 Strict-Small track. It was trained for 10 epochs on a custom 10M-word English corpus using a 32,768-token BPE vocabulary.

The model applies a shared Transformer block recursively for 16 iterations. Its hidden size is 768, embedding size is 192, and feed-forward intermediate size is 12,288. Training used Muon for the recursive block and AdamW for the embedding-related parameters, with a token batch size of 32,768 and sequence length 256.

Usage

This repository contains custom Transformers code, so loading requires trust_remote_code=True:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Serdar404/RecGPT-10M"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

The model is intended for scoring text as a causal language model. KV-cache generation is not currently implemented.

BabyLM 2026 evaluation

Final-checkpoint results before leaderboard collation:

Evaluation	Score
BLiMP	73.11
BLiMP Supplement	61.73
EWoK	52.62
Entity Tracking	16.59
COMPS	55.43
GlobalPIQA	40.68
(Super)GLUE	66.64
NLP Average	52.40

Intermediate Strict-Small checkpoints are published as Hub revisions named chck_1M through chck_100M using the official BabyLM checkpoint schedule.

Resources

• Training code: https://github.com/serdardoesml/bblm26-recgpt
• Dataset construction: https://github.com/serdardoesml/bblm26-dataset
• Evaluation fork: https://github.com/serdardoesml/babylm-eval

Limitations

This is a small research model trained under the BabyLM data constraint. It is not intended for production deployment, factual question answering, or safety-critical use. Its outputs may contain inaccuracies or undesirable content inherited from its training data.