THINK ON YOUR FEET: ADAPTIVE THINKING VIA RE-INFORCEMENT LEARNING FOR SOCIAL AGENTS

The paper presents a clear problem statement, a well-defined proposed solution, and a logical progression from theoretical inspiration to algorithmic design, experimental setup, results, and analysis. Arguments are consistent and build upon each other effectively.

Strengths: Detailed description of the AML framework, AMPO algorithm, thinking modes, and reward functions., Comprehensive two-phase training strategy (BC and RL) is outlined., Experiments include comparisons against a diverse set of strong baselines (LLMs, LRMs, Dialogue Planning methods)., Ablation studies are conducted to evaluate the contribution of different components., Statistical significance of results is mentioned., Human evaluation is performed to validate LLM-based results and check for reward hacking., Code and data availability is stated for reproducibility.
Weaknesses: Specific statistical tests used for significance testing are not explicitly named., Raw p-values are not reported, only the threshold., Detailed hyperparameter tuning procedures are not fully elaborated beyond the final settings provided in tables.

The paper provides substantial evidence through extensive experiments on two benchmark datasets (SOTOPIA, SOTOPIA-Hard) using different LLM backbones. Performance metrics (GOAL, OVERALL, Tokens) are reported quantitatively and analyzed. Ablation studies isolate component contributions, and human evaluation provides external validation, strongly supporting the claims made.

The core contribution of applying and adapting Long-CoT reasoning for adaptive thinking in social agents is novel. The proposed AML framework and the AMPO algorithm, incorporating mode-level information for dynamic mode switching, represent an original approach in this domain. The design of hierarchical thinking modes tailored for social contexts is also a novel aspect.

The research addresses a critical challenge in developing sophisticated social AI agents and demonstrates significant performance improvements and token efficiency gains over existing methods. The proposed framework and algorithm could substantially impact the field of conversational AI and the development of more human-like and capable language agents in interactive environments.

Strengths: Formal and precise academic language is used throughout., Key concepts like AML, AMPO, and thinking modes are clearly defined and explained., The methodology, including training phases, reward functions, and evaluation setup, is described in detail., Analysis sections (RQ1-RQ4) are well-structured and easy to understand.
Areas for Improvement: Some technical terms may require prior knowledge in RL and LLMs., Sentence structure can be complex in places, potentially requiring re-reading for full comprehension.