mlsanityData to 14 Oct 2026

Once Upon a Time: Interactive Learning for Storytelling with Small Language Models

arXiv 2509.15714 ↗First posted 19 Sep 2025cs.CL cs.AIcited by 11

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Abstract (arXiv)

Children efficiently acquire language not just by listening, but by interacting with others in their social environment. Conversely, large language models are typically trained with next-word prediction on massive amounts of text. Motivated by this contrast, we investigate whether language models can be trained with less data by learning not only from next-word prediction but also from high-level, cognitively inspired feedback. We train a student model to generate stories, which a teacher model rates on readability, narrative coherence, and creativity. By varying the amount of pretraining before the feedback loop, we assess the impact of this interactive learning on formal and functional linguistic competence. We find that the high-level feedback is highly data efficient: With just 1 M words of input in interactive learning, storytelling skills can improve as much as with 410 M words of next-word prediction.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 25 of its 65 references that are in the map. First posted on arXiv 19 Sep 2025.

Builds on

25 of 65 references.

References · 25

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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  3. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,183
  4. 2018Hierarchical Neural Story Generationcited by 1,193
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  8. 2019Do Massively Pretrained Language Models Make Better Storytellers?cited by 90
  9. 2025Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 88
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13 more
  1. 2024The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunitiescited by 64
  2. 2024Anisotropy Is Inherent to Self-Attention in Transformerscited by 63
  3. 2021OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metricscited by 61
  4. 2023The Next Chapter: A Study of Large Language Models in Storytellingcited by 58
  5. 2024Elements of World Knowledge (EWoK): A Cognition-Inspired Framework for Evaluating Basic World Knowledge in Language Modelscited by 55
  6. 2024Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corporacited by 48
  7. 2023Language acquisition: do children and language models follow similar learning stages?cited by 41
  8. 2025BabyLM Turns 3: Call for papers for the 2025 BabyLM workshopcited by 24
  9. 2023Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalizationcited by 16
  10. 2024Mitigating Frequency Bias and Anisotropy in Language Model Pre-Training with Syntactic Smoothingcited by 10
  11. 2023BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?cited by 7
  12. 2025Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Modelscited by 4
  13. 2025Quantifying Label-Induced Bias in Large Language Model Self- and Cross-Evaluationscited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2026Structural priors for data-efficient language learningcited by 1
  2. 2025Language models as tools for investigating the distinction between possible and impossible natural languagescited by 0
  3. 2025Large language models have learned to use languagecited by 0
  4. 2025Large language models are not about natural languagecited by 0
  5. 2025Linguists should learn to love speech-based deep learning modelscited by 0
  6. 2026Are Language Models Models?cited by 0
  7. 2026Across the Levels of Analysis: Explaining Predictive Processing in Humans Requires More Than Machine-Estimated Probabilitiescited by 0
  8. 2026You Can't Fight in Here! This is BBS!cited by 0
  9. 2026Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appearcited by 0
  10. 2026Jiuge-Tuiqiao: An Interpretable Human-AI System for Classical Chinese Poetry Refinementcited by 0
  11. 2026Which Forms of Caregiver Feedback Support Grammar Learning? A Reinforcement-Learning Study of Child-Like Language Modelscited by 0