---
title: "Towards Developmentally Plausible Rewards: Communicative"
description: "The authors design a text-only reference game. 84 references, cited by 4 papers here. Among them: 0."
authors: ["Lennart Stopler", "Rufat Asadli", "Mitja Nikolaus", "Ryan Cotterell", "Alex Warstadt"]
year: 2025
venue: "arXiv:2505.05970"
source: "https://arxiv.org/abs/2505.05970"
doi: "10.48550/arxiv.2505.05970"
arxiv: "2505.05970"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/stopler-2025-towards-developmentally-plausible-rewards-communicative/"
---
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# Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models

**Authors:** Lennart Stopler, Rufat Asadli, [Mitja Nikolaus](https://md.research.elamur.ai/free/en/authors/mitja-nikolaus/), [Ryan Cotterell](https://md.research.elamur.ai/free/en/authors/ryan-cotterell/) and [Alex Warstadt](https://md.research.elamur.ai/free/en/authors/alex-warstadt/) · **Year:** 2025 · **Venue:** arXiv:2505.05970 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2505.05970) · [PDF](https://arxiv.org/pdf/2505.05970) · [DOI 10.48550/arxiv.2505.05970](https://doi.org/10.48550/arxiv.2505.05970) · [arXiv 2505.05970](https://arxiv.org/abs/2505.05970) · [OpenAlex](https://openalex.org/W4417085895)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/stopler-2025-towards-developmentally-plausible-rewards-communicative/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Language models learn by predicting the next token, while children also learn from whether their attempts to communicate succeed. This paper asks whether communicative success can act as a reward for training LMs in a more child-like way. The authors design a text-only reference game. A speaker model, standing in for a child, condenses a passage into a short message. A fixed, fully trained listener must then answer questions about the passage using only that message, and correct answers earn the speaker a reward. A feasibility study shows that listener success drops when its input is ungrammatical, so the reward carries an indirect signal about grammar. The authors then train T5 speakers with reinforcement learning, both from scratch and by fine-tuning. They add a channel cost based on either message length or listener surprisal. This cost changes speaker behaviour in interpretable ways: length pressure produces telegraphic messages that drop function words. However, no configuration improves grammatical ability. The study is a proof of concept for interaction-based, developmentally plausible LM training.

## Key points
- **Setup:** The game is a single-turn, language-only question–answering task, unlike earlier image–caption reference games. Summarization training is interleaved with likelihood-based training on the CNN Daily Mail dataset.
- **Bottleneck sweep:** The penalty weight λ was tested at {0, 0.1, 0.5, 0.9, 1} for both the length and surprisal bottlenecks, with three random seeds per configuration.
  - Reward falls below its starting level at λ ≥ 0.5 for the length bottleneck.
  - For the surprisal bottleneck, this happens only at λ ≥ 0.9, so surprisal costs fit better with good summaries.
- **No-penalty behaviour:** With λ = 0, summaries grow from about 50% to over 70% of the original passage's length or surprisal and drift toward copying. No model fully converges on verbatim copying.
- **Grammar evaluation:** Neither LanguageTool error counts nor BLiMP improved. T5's BLiMP scores stayed near chance (about 0.5) even before training.
- **Stated limitations:** Training ran for only 240 steps, and the QA training data was roughly 500k tokens. The authors propose broader hyperparameter searches, architectures other than T5, length-bottleneck curricula, and multi-turn exchanges.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Lennart Stopler, Rufat Asadli, Mitja Nikolaus, Ryan Cotterell and Alex Warstadt. 2025. Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models. arXiv:2505.05970.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{stopler2025developmentally,
  title = {{Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models}},
  author = {Lennart Stopler, Rufat Asadli, Mitja Nikolaus, Ryan Cotterell and Alex Warstadt},
  year = {2025},
  doi = {10.48550/arxiv.2505.05970},
  eprint = {2505.05970},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.05970}
}
```

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84. Surprisal Bottleneck λ Response Reward 0.0 [find](https://scholar.google.com/scholar?q=Surprisal+Bottleneck+%CE%BB+Response+Reward+0%2E0)

*Updated 31 October 2026*