Adaptive Thinking via Mode Policy Optimization for Social Language Agents (2505.02156v4)

Abstract: Effective social intelligence simulation requires language agents to dynamically adjust reasoning depth, a capability notably absent in current studies. Existing methods either lack this kind of reasoning capability or enforce Long Chain-of-Thought reasoning uniformly across all scenarios, resulting in excessive token usage and inflexible social simulation. To address this, we propose an $\textbf{A}$daptive $\textbf{M}$ode $\textbf{L}$earning ($\textbf{AML}$) framework in this paper, aiming to improve the adaptive thinking ability of language agents in dynamic social interactions. To this end, we first identify hierarchical thinking modes ranging from intuitive response to deep deliberation based on the cognitive control theory. We then develop the $\textbf{A}$daptive $\textbf{M}$ode $\textbf{P}$olicy $\textbf{O}$ptimization ($\textbf{AMPO}$) algorithm to optimize the context-aware mode switching and reasoning. Our framework advances existing research in three key aspects: (1) Multi-granular thinking mode design, (2) Context-aware mode switching across social interaction, and (3) Token-efficient reasoning via depth-adaptive processing. Extensive experiments on social intelligence benchmarks verify that AML achieves 15.6% higher task performance than GPT-4o. Notably, our AMPO outperforms GRPO by 7.0% with 32.8% shorter reasoning chains, demonstrating the advantage of adaptive thinking mode selection and optimization mechanism in AMPO over GRPO's fixed-depth solution.

Overview of "Think on your Feet: Adaptive Thinking via Reinforcement Learning for Social Agents"

The paper "Think on your Feet: Adaptive Thinking via Reinforcement Learning for Social Agents" by Minzheng Wang and collaborators addresses a critical gap in the development of social intelligence within AI LLMs. Traditional LLMs typically excel in static tasks with deterministic solutions but falter in the dynamic, often ambiguous environments of social interactions. This paper proposes an innovative framework, Adaptive Mode Learning (AML), leveraging reinforcement learning to imbue social agents with the capability to dynamically adjust their reasoning depth according to real-time contexts. At the heart of this framework lies the Adaptive Mode Policy Optimization (AMPO) algorithm, designed to optimize context-sensitive decision-making in social interactions.

Contributions

1. Adaptive Mode Learning Framework (AML): The AML framework introduces a novel approach for simulating human-like adaptive reasoning in social agents. It utilizes the concept of dynamic mode switching, inspired by the Hierarchical Cognitive Control Theory, to simulate various depths of thought processes ranging from intuitive reactions to deep contemplations. This setup helps social agents flexibly adjust their cognitive strategies as per the demands of different scenarios.
2. Adaptive Mode Policy Optimization (AMPO): AMPO represents a significant advancement over existing methods by not only focusing on deep reasoning processes but also incorporating context-aware mode selection. This innovation ensures efficient token usage, a critical factor in managing computational costs for LLMs. The method achieved substantial improvements, notably outperforming previously leading techniques such as GRPO by 7.0% while reducing reasoning chain lengths by 32.8%.
3. Extensive Benchmarking: The framework was tested against state-of-the-art methods on various social intelligence tasks within the SOTOPIA environment. AML demonstrated superior task performance, achieving a 15.6% enhancement over existing benchmarks. This empirical validation underscores the importance of adaptive reasoning in enabling more human-like interactions in AI-powered social agents.

Implications and Future Directions

The introduction of adaptive reasoning in LLMs marks a promising shift towards more efficient and context-sensitive AI systems. The practical implications are substantial, particularly in fields requiring nuanced social interactions such as negotiation, collaboration, and conflict resolution. Theoretically, this research opens avenues for exploring more refined cognitive architectures in AI, prompting deeper investigations into how artificial systems can better emulate human thought processes.

Future research could build upon this foundation by exploring further adaptive reasoning mechanisms, potentially incorporating additional layers of cognitive control and abstraction as outlined in cognitive sciences. Additionally, extending these methods to other dynamic domains beyond social interactions, such as real-time decision-making in autonomous systems, could offer broader applicability and enhance the robustness of AI models.

In conclusion, this paper makes noteworthy contributions to the development of adaptive social intelligence in LLMs, paving the way for more context-sensitive and efficient AI systems capable of managing real-world complexities akin to human reasoning.