Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models

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Main Authors: Stöpler, Lennart, Asadli, Rufat, Nikolaus, Mitja, Cotterell, Ryan, Warstadt, Alex
Format: Preprint
Published: 2025
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author Stöpler, Lennart
Asadli, Rufat
Nikolaus, Mitja
Cotterell, Ryan
Warstadt, Alex
author_facet Stöpler, Lennart
Asadli, Rufat
Nikolaus, Mitja
Cotterell, Ryan
Warstadt, Alex
contents We propose a method for training language models in an interactive setting inspired by child language acquisition. In our setting, a speaker attempts to communicate some information to a listener in a single-turn dialogue and receives a reward if communicative success is achieved. Unlike earlier related work using image--caption data for interactive reference games, we operationalize communicative success in a more abstract language-only question--answering setting. First, we present a feasibility study demonstrating that our reward provides an indirect signal about grammaticality. Second, we conduct experiments using reinforcement learning to fine-tune language models. We observe that cognitively plausible constraints on the communication channel lead to interpretable changes in speaker behavior. However, we do not yet see improvements on linguistic evaluations from our training regime. We outline potential modifications to the task design and training configuration that could better position future work to use our methodology to observe the benefits of interaction on language learning in computational cognitive models.
format Preprint
id arxiv_https___arxiv_org_abs_2505_05970
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models
Stöpler, Lennart
Asadli, Rufat
Nikolaus, Mitja
Cotterell, Ryan
Warstadt, Alex
Computation and Language
We propose a method for training language models in an interactive setting inspired by child language acquisition. In our setting, a speaker attempts to communicate some information to a listener in a single-turn dialogue and receives a reward if communicative success is achieved. Unlike earlier related work using image--caption data for interactive reference games, we operationalize communicative success in a more abstract language-only question--answering setting. First, we present a feasibility study demonstrating that our reward provides an indirect signal about grammaticality. Second, we conduct experiments using reinforcement learning to fine-tune language models. We observe that cognitively plausible constraints on the communication channel lead to interpretable changes in speaker behavior. However, we do not yet see improvements on linguistic evaluations from our training regime. We outline potential modifications to the task design and training configuration that could better position future work to use our methodology to observe the benefits of interaction on language learning in computational cognitive models.
title Towards Developmentally Plausible Rewards: Communicative Success as a Learning Signal for Interactive Language Models
topic Computation and Language
url https://arxiv.org/abs/2505.05970