While Reinforcement Learning (RL) has been proven essential for tuning large language models (LLMs), it can lead to reward over-optimization (ROO). Existing approaches address ROO by adding KL regularization, requiring computationally expensive hyperparameter tuning. Additionally, KL regularization focuses solely on regularizing the language policy, neglecting a potential source of regularization: the reward function itself. Inspired by demonstration-guided RL, we here introduce the Reward Calibration from Demonstration (RCfD), which leverages human demonstrations and a reward model to recalibrate the reward objective. Formally, given a prompt, the RCfD objective minimizes the distance between the demonstrations' and LLM's rewards rather than directly maximizing the reward function. This objective shift avoids incentivizing the LLM to exploit the reward model and promotes more natural and diverse language generation. We show the effectiveness of RCfD on three language tasks, which achieves comparable performance to carefully tuned baselines while mitigating ROO.

Social animals have been found to use some means of communication to coordinate in various contexts: foraging for food, avoiding predators, mating, etc. (Hauser, 1996). Among animals, however, humans seem to be unique in having developed a communication system, natural language, that transcends these basic needs and can represent an infinite variety of new situations (Hauser et al., 2002) to the extent that language itself becomes the basis for a new form of evolution: cultural evolution. Understanding the emergence of this unique human ability has always been a vexing scientific problem due to the lack of access to the communication systems of intermediate steps of hominid evolution (Harnad et al., 1976; Bickerton, 2007). In the absence of data, a tempting idea has been to reproduce experimentally the process of language emergence in either humans or computational models (Steels, 1997; Myers-Scotton, 2002; Kirby, 2002). Experimental paradigms with humans (Kirby et al., 2008; Raviv et al., 2019; Motamedi et al., 2019) have produced significant insights into language evolution. Still, their scope is limited due to the inability to replicate key aspects of language evolution, such as communication within and across large populations and the study of long evolutionary timescales. Computer modeling can help overcome these limitations and has played a prominent role in studying language evolution for a long time (Lieberman and Crelin, 1971).

Compositionality is a hallmark of human language that not only enables linguistic generalization, but also potentially facilitates acquisition. When simulating language emergence with neural networks, compositionality has been shown to improve communication performance; however, its impact on imitation learning has yet to be investigated. Our work explores the link between compositionality and imitation in a Lewis game played by deep neural agents. Our contributions are twofold: first, we show that the learning algorithm used to imitate is crucial: supervised learning tends to produce more average languages, while reinforcement learning introduces a selection pressure toward more compositional languages. Second, our study reveals that compositional languages are easier to imitate, which may induce the pressure toward compositional languages in RL imitation settings.

Lewis signaling games are a class of simple communication games for simulating the emergence of language. In these games, two agents must agree on a communication protocol in order to solve a cooperative task. Previous work has shown that agents trained to play this game with reinforcement learning tend to develop languages that display undesirable properties from a linguistic point of view (lack of generalization, lack of compositionality, etc). In this paper, we aim to provide better understanding of this phenomenon by analytically studying the learning problem in Lewis games. As a core contribution, we demonstrate that the standard objective in Lewis games can be decomposed in two components: a co-adaptation loss and an information loss. This decomposition enables us to surface two potential sources of overfitting, which we show may undermine the emergence of a structured communication protocol. In particular, when we control for overfitting on the co-adaptation loss, we recover desired properties in the emergent languages: they are more compositional and generalize better.

Crucially, ZLA is considered to be an efficient property Previous work has shown that artificial of our language (Gibson et al., 2019). Besides the neural agents naturally develop surprisingly obvious fact that an efficient code would be easier non-efficient codes. This is illustrated to process for us, it is also argued to be a core property by the fact that in a referential of natural language, likely to be correlated game involving a speaker and a listener with other fundamental aspects of human communication, neural networks optimizing accurate transmission such as regularity and compositionality over a discrete channel, the emergent (Kirby, 2001). Encouraging it might hence lead messages fail to achieve an optimal to emergent languages that are also more likely to length. Furthermore, frequent messages develop these other desirable properties.