We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structure and order helps guide exploration towards a subspace of tasks that are not favored by naive uncertainty-based intrinsic rewards. Our generalized formulation of Regularity as Intrinsic Reward (RaIR) allows us to operationalize it within model-based reinforcement learning. In a synthetic environment, we showcase the plethora of structured patterns that can emerge from pursuing this regularity objective. We also demonstrate the strength of our method in a multi-object robotic manipulation environment. We incorporate RaIR into free play and use it to complement the model's epistemic uncertainty as an intrinsic reward. Doing so, we witness the autonomous construction of towers and other regular structures during free play, which leads to a substantial improvement in zero-shot downstream task performance on assembly tasks.

Chinese Spelling Correction (CSC) aims to detect and correct erroneous tokens in sentences. Traditional CSC focuses on equal length correction and uses pretrained language models (PLMs). While Large Language Models (LLMs) have shown remarkable success in identifying and rectifying potential errors, they often struggle with adapting to domain-specific corrections, especially when encountering terminologies in specialized domains. To address domain adaptation, we propose a \textbf{R}etrieval-\textbf{A}ugmented \textbf{I}terative \textbf{R}efinement (RAIR) framework. Our approach constructs a retrieval corpus adaptively from domain-specific training data and dictionaries, employing a fine-tuned retriever to ensure that the retriever catches the error correction pattern. We also extend equal-length into variable-length correction scenarios. Extensive experiments demonstrate that our framework outperforms current approaches in domain spelling correction and significantly improves the performance of LLMs in variable-length scenarios.

We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structure and order helps guide exploration towards a subspace of tasks that are not favored by naive uncertainty-based intrinsic rewards. Our generalized formulation of Regularity as Intrinsic Reward (RaIR) allows us to operationalize it within model-based reinforcement learning. In a synthetic environment, we showcase the plethora of structured patterns that can emerge from pursuing this regularity objective. We also demonstrate the strength of our method in a multi-object robotic manipulation environment.

The potential is not necessarily realized, however, because of several challenges: debates on ethical resposibility of decisions [8, 26, 44], the human ability to recognize when AI advice should be taken [43], mental models (biases) regarding AI performance and ability [12, 27] to perform well on subjective tasks, and effects of how the AI advice is delivered [46]. Many research directions thus aim to resolve these barriers to complementarity in human-AI performance, including examining the effects of having AI systems explain predictions [4] using explainable AI (XAI) methods, introducing cognitive forcing functions when presenting AI advice [6], adjusting AI advice interactions/presentation methods [40], and adjusting task framing to account for mental models about the types of tasks AI can work with [9]. In AI-assisted decision making, the human makes the final decision, bearing full responsibility for its consequences. Performance pressure from responsibility can influence decision making behavior [2]. The bulk of research working towards complementary human-AI performance isolates human behavior away from the effects of performance pressure because the field is rapidly evolving its understanding of how humans perceive and work with AI tools. Intrinsically high and low stakes tasks are used in these experiments, but the stakes have little tangible effect or implication for evaluators. Hence, we observe a gap in the literature of how people rely on AI assistants under performance pressure, or when stakes matter personally. In this work, we seek to understand how performance pressure affects AI advice usage when AI advice is provided as a second opinion. We induce performance pressure through a pay-by-performance scheme framed as a loss.

We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structure and order helps guide exploration towards a subspace of tasks that are not favored by naive uncertainty-based intrinsic rewards. Our generalized formulation of Regularity as Intrinsic Reward (RaIR) allows us to operationalize it within model-based reinforcement learning. In a synthetic environment, we showcase the plethora of structured patterns that can emerge from pursuing this regularity objective. We also demonstrate the strength of our method in a multiobject robotic manipulation environment. We incorporate RaIR into free play and use it to complement the model's epistemic uncertainty as an intrinsic reward. Doing so, we witness the autonomous construction of towers and other regular structures during free play, which leads to a substantial improvement in zero-shot downstream task performance on assembly tasks. Code and videos are available at https://sites.google.com/view/rair-project. Figure 1: Regularity as intrinsic reward yields ordered and symmetric patterns.