blm-gpt2s-90M-s42

Model Card for the Preference Optimization Interaction Baseline

<!-- Provide a quick summary of what the model is/does. [Optional] -->

A 124M model with the GPT-2 architecture trained with the next token prediction loss for 10 epochs (~900M words) on 90% of the BabyLM corpus, as a naive autoregressive baseline for the Interaction track of the 2025 BabyLM challenge.

This model card is based on the model card of the BabyLM 100M GPT-2 baseline.

Table of Contents

• Model Card for Interaction GPT-2 Baseline
• Table of Contents
• Model Details
  • Model Description
• Uses
• Training Details
  • Training Data
  • Hyperparameters
  • Training Procedure
    • Size and Checkpoints
• Evaluation
  • Testing Data & Metrics
    • Testing Data
    • Metrics
  • Results
• Technical Specifications
  • Model Architecture and Objective
  • Compute Infrastructure
    • Hardware
    • Software
    • Training Time
• Citation
• Model Card Authors
• Bibliography

Model Details

Model Description

<!-- Provide a longer summary of what this model is/does. -->

This is the pretrained GPT-2 model as a basis for PPO finetuning for the Interaction Track of the 2025 BabyLM challenge.

• Developed by: Jonas Mayer Martins, Ali Hamza Bashir, Muhammad Rehan Khalid
• Model type: Causal language model
• Language(s) (NLP): eng
• Resources for more information:
  • GitHub Repo

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This is a pre-trained language model. It can be used to evaluate tasks in a zero-shot manner and also can be fine-tuned for downstream tasks. It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance.

Training Details

Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

We used the BabyLM 100M (Strict) dataset for training. We trained the tokenizer and model on randomly selected 90% of the corpus, which is composed of the following:

¹ Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).

Hyperparameters

Training Procedure

The model is trained with next token prediction loss for 10 epochs.

Size and checkpoints

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

The model has 124M parameters. In total we train on around 1B words and provide multiple checkpoints from the training. Specifically we provode:

• Checkpoints every 1M words for the first 10M words
• Checkpoints every 10M words first 100M words
• Checkpoints every 100M words until 1B words

Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

This model is evaluated in two ways:

1. We do zero-shot evaluation on 7 tasks.
2. We do fine-tuning on a subset of the (Super)GLUE tasks (Wang et al., ICLR 2019; Wang et al., NeurIPS 2019) .

Testing Data & Metrics

Testing Data

<!-- This should link to a Data Card if possible. -->

For the BLiMP, BLiMP supplement, and EWoK tasks, we use a filtered version of the dataset to only include examples with words found in the BabyLM dataset. For the Finetuning task, we both filter and sample down to a maximum 10 000 train examples.

Validation Data

Zero-shot Tasks

• BLiMP: The Benchmark of Linguistic Minimal Pairs evaluates the model's linguistic ability by seeing if it can recognize the grammatically correct sentence from a pair of minimally different sentences. It tests various grammatical phenomena.(Warstadt et al., TACL 2020)
• BLiMP Supplement: A supplement to BLiMP introduced in the first edition of the BabyLM challenge. More focused on dialogue and questions. (Warstadt et al., CoNLL-BabyLM 2023)
• EWoK: Works similarly to BLiMP but looks the model's internal world knowledge. Looking at both whter a model has physical and social knowledge. (Ivanova et al., 2024)
• Eye Tracking and Self-paced Reading: Looks at whether the model can mimick the eye tracking and reading time of a human but using surprisal of a word as a proxy for time spent reading a word. (de Varda et al., BRM 2024)
• Entity Tracking: Checks whether a model can keep track of the changes to the states of entities as text/dialogue unfolds. (Kim & Schuster, ACL 2023)
• WUGs: Tests morphological generalization in LMs through an adjective nominalization and past tense task. (Hofmann et al., 2024) (Weissweiler et al., 2023)
• COMPS: Property knowledge. (Misra et al., 2023)

Finetuning Tasks

• BoolQ: A yes/no QA dataset with unprompted and unconstrained questions. (Clark et al., NAACL 2019)
• MNLI: The Multi-Genre Natural Language Inference corpus tests the language understanding of a model by seeing wehther it can recognize textual entailment. (Williams et al., NAACL 2018)
• MRPC: The Microsoft Research Paraphrase Corpus contains pairs of sentences that are either paraphrases/semntically equivalent to each other or unrelated.(Dolan & Brockett, IJCNLP 2005)
• QQP²: Similarly to MRPC, the Quora Question Pairs corpus tests the models ability to determine whether a pair of questions are sematically similar to each other. These questions are sourced from Quora.
• MultiRC: The Multi-Sentence Reading Comprehension corpus is a QA task that evaluates the model's ability to the correct answer from a list of answers given a question and context paragraph. In this version the data is changed to a binary classification judging whether the answer to a question, context pair is correct. (Khashabi et al., NAACL 2018)
• RTE: Similar the Recognizing Text Entailement tests the model's ability to recognize text entailement. (Dagan et al., Springer 2006; Bar et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., TAC 2009)
• WSC: The Winograd Schema Challenge tests the models ability to do coreference resolution on sentences with a pronoun and a list of noun phrases found in the sentence. This version edits it to be a binary classification on examples consisting of a pronoun and noun phrase.(Levesque et al., PKRR 2012)

² https://www.quora.com/profile/Ricky-Riche-2/First-Quora-Dataset-Release-Question-Pairs

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

The metrics used to evaluate the model are the following:

• Zero-shot
  • Accuracy on predicting the correct completion/sentence for BLiMP, BLiMP Supplement, EWoK, Entity Tracking, and WUGs
  • Change in R^2 prediction from baseline for Eye Tracking (with no spillover) and Self-paced Reading (1-word spillover)
• Finetuning
  • 3 class Accuracy for MNLI
  • Binary Accuracy for BoolQ, MultiRC, and WSC
  • F1-score for MRPC and QQP

The metrics were chosen based on the advice of the papers the tasks come from.

Hyperparameters

Results

We compare our student model against two official baselines from the 2025 BabyLM Challenge¹:

• 1000M-pre: The standard pretraining baseline, using a GPT-2-small model trained on 100M unique words from the BabyLM dataset (10 epochs, next-word prediction).
• SimPO: A baseline first trained for 7 epochs with next-word prediction, then 2 epochs interleaving prediction and reinforcement learning. Here, the RL reward encourages the student to generate completions similar to the teacher’s output.
• 900M-pre: Our model, using the same GPT-2-small architecture, pretrained on 90% of the BabyLM dataset (yielding approximately 91M unique words, 10 epochs).
• 900M-RL: Our model after additional PPO-based reinforcement learning with the teacher, using about 1M words as input for the interactive (RL) phase.

---

Evaluation Results

Model descriptions:

• 1000M-pre: Baseline pretrained on 100M words (BabyLM challenge baseline).
• SimPO: Baseline using a hybrid of pretraining and RL with a similarity-based reward.
• 900M-pre: Our GPT-2-small model, pretrained on 90M words (similar settings as baseline, but less data).
• 900M-RL: The same model as 900M-pre, further trained with PPO using teacher feedback on 1M words of input.

See: BabyLM Challenge for the baselines.

Technical Specifications

Hardware

• 4 A100 GPUs were used to train this model.

Software

PyTorch

Training Time

The model took 2.5 hours to train and consumed 755 core hours (with 4 GPUs and 32 CPUs).

Citation

@misc{MayerMartinsBKB2025,
      title={Once Upon a Time: Interactive Learning for Storytelling with Small Language Models}, 
      author={Jonas Mayer Martins, Ali Hamza Bashir, Muhammad Rehan Khalid, Lisa Beinborn},
      year={2025},
      eprint={2502.TODO},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={ToDo}, 
}

Model Card Authors

Jonas Mayer Martins

Bibliography

GLUE: A multi-task benchmark and analysis platform for natural language understanding (Wang et al., ICLR 2019)

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems (Wang et al., NeurIPS 2019)

BLiMP: The Benchmark of Linguistic Minimal Pairs for English (Warstadt et al., TACL 2020)

Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (Warstadt et al., CoNLL-BabyLM 2023)

🌏 Elements of World Knowledge (EWoK): A cognition-inspired framework for evaluating basic world knowledge in language models (Ivanova et al., 2024)

Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data (de Varda et al., BRM 2024)

Entity Tracking in Language Models (Kim & Schuster, ACL 2023)

Derivational Morphology Reveals Analogical Generalization in Large Language Models (Hofmann et al., 2024)

Automatically Constructing a Corpus of Sentential Paraphrases (Dolan & Brockett, IJCNLP 2005)

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference (Williams et al., NAACL 2018)

The Winograd Schema Challenge (Levesque et al., PKRR 2012)

The PASCAL Recognising Textual Entailment Challenge (Dagan et al., Springer 2006)

The Second PASCAL Recognising Textual Entailment Challenge (Bar et al., 2006)

The Third PASCAL Recognizing Textual Entailment Challenge (Giampiccolo et al., 2007)

The Fifth PASCAL Recognizing Textual Entailment Challenge (Bentivogli et al., TAC 2009)

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions (Clark et al., NAACL 2019)

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (Khashabi et al., NAACL 2018)